Predicting Air Quality in Smart Environments

PubMed Central

Deleawe, Seun; Kusznir, Jim; Lamb, Brian; Cook, Diane J.

2011-01-01

The pervasive sensing technologies found in smart environments offer unprecedented opportunities for monitoring and assisting the individuals who live and work in these spaces. As aspect of daily life that is often overlooked in maintaining a healthy lifestyle is the air quality of the environment. In this paper we investigate the use of machine learning technologies to predict CO2 levels as an indicator of air quality in smart environments. We introduce techniques for collecting and analyzing sensor information in smart environments and analyze the correlation between resident activities and air quality levels. The effectiveness of our techniques is evaluated using three physical smart environment testbeds. PMID:21617739

Urban air quality forecasting based on multi-dimensional collaborative Support Vector Regression (SVR): A case study of Beijing-Tianjin-Shijiazhuang

PubMed Central

Liu, Bing-Chun; Binaykia, Arihant; Chang, Pei-Chann; Tiwari, Manoj Kumar; Tsao, Cheng-Chin

2017-01-01

Today, China is facing a very serious issue of Air Pollution due to its dreadful impact on the human health as well as the environment. The urban cities in China are the most affected due to their rapid industrial and economic growth. Therefore, it is of extreme importance to come up with new, better and more reliable forecasting models to accurately predict the air quality. This paper selected Beijing, Tianjin and Shijiazhuang as three cities from the Jingjinji Region for the study to come up with a new model of collaborative forecasting using Support Vector Regression (SVR) for Urban Air Quality Index (AQI) prediction in China. The present study is aimed to improve the forecasting results by minimizing the prediction error of present machine learning algorithms by taking into account multiple city multi-dimensional air quality information and weather conditions as input. The results show that there is a decrease in MAPE in case of multiple city multi-dimensional regression when there is a strong interaction and correlation of the air quality characteristic attributes with AQI. Also, the geographical location is found to play a significant role in Beijing, Tianjin and Shijiazhuang AQI prediction. PMID:28708836

Meteorological Processes Affecting Air Quality – Research and Model Development Needs

EPA Science Inventory

Meteorology modeling is an important component of air quality modeling systems that defines the physical and dynamical environment for atmospheric chemistry. The meteorology models used for air quality applications are based on numerical weather prediction models that were devel...

Impact of inherent meteorology uncertainty on air quality model predictions

EPA Science Inventory

It is well established that there are a number of different classifications and sources of uncertainties in environmental modeling systems. Air quality models rely on two key inputs, namely, meteorology and emissions. When using air quality models for decision making, it is impor...

Development of visibility forecasting modeling framework for the Lower Fraser Valley of British Columbia using Canada's Regional Air Quality Deterministic Prediction System.

PubMed

So, Rita; Teakles, Andrew; Baik, Jonathan; Vingarzan, Roxanne; Jones, Keith

2018-05-01

Visibility degradation, one of the most noticeable indicators of poor air quality, can occur despite relatively low levels of particulate matter when the risk to human health is low. The availability of timely and reliable visibility forecasts can provide a more comprehensive understanding of the anticipated air quality conditions to better inform local jurisdictions and the public. This paper describes the development of a visibility forecasting modeling framework, which leverages the existing air quality and meteorological forecasts from Canada's operational Regional Air Quality Deterministic Prediction System (RAQDPS) for the Lower Fraser Valley of British Columbia. A baseline model (GM-IMPROVE) was constructed using the revised IMPROVE algorithm based on unprocessed forecasts from the RAQDPS. Three additional prototypes (UMOS-HYB, GM-MLR, GM-RF) were also developed and assessed for forecast performance of up to 48 hr lead time during various air quality and meteorological conditions. Forecast performance was assessed by examining their ability to provide both numerical and categorical forecasts in the form of 1-hr total extinction and Visual Air Quality Ratings (VAQR), respectively. While GM-IMPROVE generally overestimated extinction more than twofold, it had skill in forecasting the relative species contribution to visibility impairment, including ammonium sulfate and ammonium nitrate. Both statistical prototypes, GM-MLR and GM-RF, performed well in forecasting 1-hr extinction during daylight hours, with correlation coefficients (R) ranging from 0.59 to 0.77. UMOS-HYB, a prototype based on postprocessed air quality forecasts without additional statistical modeling, provided reasonable forecasts during most daylight hours. In terms of categorical forecasts, the best prototype was approximately 75 to 87% correct, when forecasting for a condensed three-category VAQR. A case study, focusing on a poor visual air quality yet low Air Quality Health Index episode, illustrated that the statistical prototypes were able to provide timely and skillful visibility forecasts with lead time up to 48 hr. This study describes the development of a visibility forecasting modeling framework, which leverages the existing air quality and meteorological forecasts from Canada's operational Regional Air Quality Deterministic Prediction System. The main applications include tourism and recreation planning, input into air quality management programs, and educational outreach. Visibility forecasts, when supplemented with the existing air quality and health based forecasts, can assist jurisdictions to anticipate the visual air quality impacts as perceived by the public, which can potentially assist in formulating the appropriate air quality bulletins and recommendations.

LaSVM-based big data learning system for dynamic prediction of air pollution in Tehran.

PubMed

Ghaemi, Z; Alimohammadi, A; Farnaghi, M

2018-04-20

Due to critical impacts of air pollution, prediction and monitoring of air quality in urban areas are important tasks. However, because of the dynamic nature and high spatio-temporal variability, prediction of the air pollutant concentrations is a complex spatio-temporal problem. Distribution of pollutant concentration is influenced by various factors such as the historical pollution data and weather conditions. Conventional methods such as the support vector machine (SVM) or artificial neural networks (ANN) show some deficiencies when huge amount of streaming data have to be analyzed for urban air pollution prediction. In order to overcome the limitations of the conventional methods and improve the performance of urban air pollution prediction in Tehran, a spatio-temporal system is designed using a LaSVM-based online algorithm. Pollutant concentration and meteorological data along with geographical parameters are continually fed to the developed online forecasting system. Performance of the system is evaluated by comparing the prediction results of the Air Quality Index (AQI) with those of a traditional SVM algorithm. Results show an outstanding increase of speed by the online algorithm while preserving the accuracy of the SVM classifier. Comparison of the hourly predictions for next coming 24 h, with those of the measured pollution data in Tehran pollution monitoring stations shows an overall accuracy of 0.71, root mean square error of 0.54 and coefficient of determination of 0.81. These results are indicators of the practical usefulness of the online algorithm for real-time spatial and temporal prediction of the urban air quality.

Predicting Air Quality Impacts Associated with Oil and Gas Development in the Uinta Basin Using EPA’s Photochemical Air Quality Model

EPA Science Inventory

Rural areas with close proximity to oil and natural gas operations in Utah have experienced winter ozone levels that exceed EPA’s National Ambient Air Quality Standards (NAAQS). Through a collaborative effort, EPA Region 8 – Air Program, ORD, and OAQPS used the Commun...

Poor Air Quality Expected for New England on May 17-18, 2017

EPA Pesticide Factsheets

New England state air quality forecasters are predicting air quality that is unhealthy for sensitive groups, due to ground-level ozone, in much of CT, northern RI & portions of central and southeastern MA (excluding the Cape and the Islands) for May 17.

Use of Air Quality Observations by the National Air Quality Forecast Capability

NASA Astrophysics Data System (ADS)

Stajner, I.; McQueen, J.; Lee, P.; Stein, A. F.; Kondragunta, S.; Ruminski, M.; Tong, D.; Pan, L.; Huang, J. P.; Shafran, P.; Huang, H. C.; Dickerson, P.; Upadhayay, S.

2015-12-01

The National Air Quality Forecast Capability (NAQFC) operational predictions of ozone and wildfire smoke for the United States (U.S.) and predictions of airborne dust for continental U.S. are available at http://airquality.weather.gov/. NOAA National Centers for Environmental Prediction (NCEP) operational North American Mesoscale (NAM) weather predictions are combined with the Community Multiscale Air Quality (CMAQ) model to produce the ozone predictions and test fine particulate matter (PM2.5) predictions. The Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model provides smoke and dust predictions. Air quality observations constrain emissions used by NAQFC predictions. NAQFC NOx emissions from mobile sources were updated using National Emissions Inventory (NEI) projections for year 2012. These updates were evaluated over large U.S. cities by comparing observed changes in OMI NO2 observations and NOx measured by surface monitors. The rate of decrease in NOx emission projections from year 2005 to year 2012 is in good agreement with the observed changes over the same period. Smoke emissions rely on the fire locations detected from satellite observations obtained from NESDIS Hazard Mapping System (HMS). Dust emissions rely on a climatology of areas with a potential for dust emissions based on MODIS Deep Blue aerosol retrievals. Verification of NAQFC predictions uses AIRNow compilation of surface measurements for ozone and PM2.5. Retrievals of smoke from GOES satellites are used for verification of smoke predictions. Retrievals of dust from MODIS are used for verification of dust predictions. In summary, observations are the basis for the emissions inputs for NAQFC, they are critical for evaluation of performance of NAQFC predictions, and furthermore they are used in real-time testing of bias correction of PM2.5 predictions, as we continue to work on improving modeling and emissions important for representation of PM2.5.

Developing a smartphone software package for predicting atmospheric pollutant concentrations at mobile locations.

PubMed

Larkin, Andrew; Williams, David E; Kile, Molly L; Baird, William M

2015-06-01

There is considerable evidence that exposure to air pollution is harmful to health. In the U.S., ambient air quality is monitored by Federal and State agencies for regulatory purposes. There are limited options, however, for people to access this data in real-time which hinders an individual's ability to manage their own risks. This paper describes a new software package that models environmental concentrations of fine particulate matter (PM 2.5 ), coarse particulate matter (PM 10 ), and ozone concentrations for the state of Oregon and calculates personal health risks at the smartphone's current location. Predicted air pollution risk levels can be displayed on mobile devices as interactive maps and graphs color-coded to coincide with EPA air quality index (AQI) categories. Users have the option of setting air quality warning levels via color-coded bars and were notified whenever warning levels were exceeded by predicted levels within 10 km. We validated the software using data from participants as well as from simulations which showed that the application was capable of identifying spatial and temporal air quality trends. This unique application provides a potential low-cost technology for reducing personal exposure to air pollution which can improve quality of life particularly for people with health conditions, such as asthma, that make them more susceptible to these hazards.

Dynamic Evaluation of a Regional Air Quality Model: Assessing the Emissions-Induced Weekly Ozone Cycle

EPA Science Inventory

Air quality models are used to predict changes in pollutant concentrations resulting from envisioned emission control policies. Recognizing the need to assess the credibility of air quality models in a policy-relevant context, we perform a dynamic evaluation of the community Mult...

AIR QUALITY AND GLOBAL CLIMATE CHANGE (PHASE 1)

EPA Science Inventory

Predicted changes in the global climate over the coming decades could alter weather patterns and, thus, impact land use, source emissions, and tropospheric air quality. The United States has a series of standards for criteria air pollutants and other air pollutants in place to s...

Hadoop-Based Distributed System for Online Prediction of Air Pollution Based on Support Vector Machine

NASA Astrophysics Data System (ADS)

Ghaemi, Z.; Farnaghi, M.; Alimohammadi, A.

2015-12-01

The critical impact of air pollution on human health and environment in one hand and the complexity of pollutant concentration behavior in the other hand lead the scientists to look for advance techniques for monitoring and predicting the urban air quality. Additionally, recent developments in data measurement techniques have led to collection of various types of data about air quality. Such data is extremely voluminous and to be useful it must be processed at high velocity. Due to the complexity of big data analysis especially for dynamic applications, online forecasting of pollutant concentration trends within a reasonable processing time is still an open problem. The purpose of this paper is to present an online forecasting approach based on Support Vector Machine (SVM) to predict the air quality one day in advance. In order to overcome the computational requirements for large-scale data analysis, distributed computing based on the Hadoop platform has been employed to leverage the processing power of multiple processing units. The MapReduce programming model is adopted for massive parallel processing in this study. Based on the online algorithm and Hadoop framework, an online forecasting system is designed to predict the air pollution of Tehran for the next 24 hours. The results have been assessed on the basis of Processing Time and Efficiency. Quite accurate predictions of air pollutant indicator levels within an acceptable processing time prove that the presented approach is very suitable to tackle large scale air pollution prediction problems.

PREDICTING THE IMPACT OF TROPOSPHERIC OZONE ON ECOLOGICAL RESOURCES FOR SETTING NATIONAL AMBIENT AIR QUALITY STANDARDS

EPA Science Inventory

The Clean Air Act provides for establishing National Ambient Air Quality Standards (NAAQS) to protect public welfare (including crops, forests, ecosystems, and soils) from adverrse effects of air pollutants, including tropospheric ozone. The formulation of policies is science-bas...

Predictive monitoring and diagnosis of periodic air pollution in a subway station.

PubMed

Kim, YongSu; Kim, MinJung; Lim, JungJin; Kim, Jeong Tai; Yoo, ChangKyoo

2010-11-15

The purpose of this study was to develop a predictive monitoring and diagnosis system for the air pollutants in a subway system using a lifting technique with a multiway principal component analysis (MPCA) which monitors the periodic patterns of the air pollutants and diagnoses the sources of the contamination. The basic purpose of this lifting technique was to capture the multivariate and periodic characteristics of all of the indoor air samples collected during each day. These characteristics could then be used to improve the handling of strong periodic fluctuations in the air quality environment in subway systems and will allow important changes in the indoor air quality to be quickly detected. The predictive monitoring approach was applied to a real indoor air quality dataset collected by telemonitoring systems (TMS) that indicated some periodic variations in the air pollutants and multivariate relationships between the measured variables. Two monitoring models--global and seasonal--were developed to study climate change in Korea. The proposed predictive monitoring method using the lifted model resulted in fewer false alarms and missed faults due to non-stationary behavior than that were experienced with the conventional methods. This method could be used to identify the contributions of various pollution sources. Copyright © 2010 Elsevier B.V. All rights reserved.

Predicting Future-Year Ozone Concentrations: Integrated Observational-Modeling Approach for Probabilistic Evaluation of the Efficacy of Emission Control strategies

EPA Science Inventory

Regional-scale air quality models are being used to demonstrate attainment of the ozone air quality standard. In current regulatory applications, a regional-scale air quality model is applied for a base year and a future year with reduced emissions using the same meteorological ...

A Direct sensitivity approach to predict hourly ozone resulting from compliance with the National Ambient Air Quality Standard

EPA Science Inventory

In setting primary ambient air quality standards, the EPA’s responsibility under the law is to establish standards that protect public health. As part of the current review of the ozone National Ambient Air Quality Standard (NAAQS), the US EPA evaluated the health exposure and ...

Statistical Properties of Differences between Low and High Resolution CMAQ Runs with Matched Initial and Boundary Conditions

EPA Science Inventory

The difficulty in assessing errors in numerical models of air quality is a major obstacle to improving their ability to predict and retrospectively map air quality. In this paper, using simulation outputs from the Community Multi-scale Air Quality Model (CMAQ), the statistic...

Evaluating the capability of regional-scale air quality models to capture the vertical distribution of pollutants

EPA Science Inventory

This study is conducted in the framework of the Air Quality Modelling Evaluation International Initiative (AQMEII) and aims at the operational evaluation of an ensemble of 12 regional-scale chemical transport models used to predict air quality over the North American (NA) and Eur...

PREDICTING THE IMPACT OF TROPOSPHERIC OZONE ON PLANTS AND ECOSYSTEMS AS A BASIS FOR SETTING NATIONAL AIR QUALITY STANDARDS

EPA Science Inventory

The Clean Air Act provides for establishing National Ambient Air Quality Standards (NAAQS) to protect public welfare (including crops, forests, ecosystems, and soils) from adverse effects of air pollutants, including tropospheric ozone. The formulation of policies is science-base...

A review of AirQ Models and their applications for forecasting the air pollution health outcomes.

PubMed

Oliveri Conti, Gea; Heibati, Behzad; Kloog, Itai; Fiore, Maria; Ferrante, Margherita

2017-03-01

Even though clean air is considered as a basic requirement for the maintenance of human health, air pollution continues to pose a significant health threat in developed and developing countries alike. Monitoring and modeling of classic and emerging pollutants is vital to our knowledge of health outcomes in exposed subjects and to our ability to predict them. The ability to anticipate and manage changes in atmospheric pollutant concentrations relies on an accurate representation of the chemical state of the atmosphere. The task of providing the best possible analysis of air pollution thus requires efficient computational tools enabling efficient integration of observational data into models. A number of air quality models have been developed and play an important role in air quality management. Even though a large number of air quality models have been discussed or applied, their heterogeneity makes it difficult to select one approach above the others. This paper provides a brief review on air quality models with respect to several aspects such as prediction of health effects.

An Internet of Things System for Underground Mine Air Quality Pollutant Prediction Based on Azure Machine Learning

PubMed Central

Jo, ByungWan

2018-01-01

The implementation of wireless sensor networks (WSNs) for monitoring the complex, dynamic, and harsh environment of underground coal mines (UCMs) is sought around the world to enhance safety. However, previously developed smart systems are limited to monitoring or, in a few cases, can report events. Therefore, this study introduces a reliable, efficient, and cost-effective internet of things (IoT) system for air quality monitoring with newly added features of assessment and pollutant prediction. This system is comprised of sensor modules, communication protocols, and a base station, running Azure Machine Learning (AML) Studio over it. Arduino-based sensor modules with eight different parameters were installed at separate locations of an operational UCM. Based on the sensed data, the proposed system assesses mine air quality in terms of the mine environment index (MEI). Principal component analysis (PCA) identified CH4, CO, SO2, and H2S as the most influencing gases significantly affecting mine air quality. The results of PCA were fed into the ANN model in AML studio, which enabled the prediction of MEI. An optimum number of neurons were determined for both actual input and PCA-based input parameters. The results showed a better performance of the PCA-based ANN for MEI prediction, with R2 and RMSE values of 0.6654 and 0.2104, respectively. Therefore, the proposed Arduino and AML-based system enhances mine environmental safety by quickly assessing and predicting mine air quality. PMID:29561777

An Internet of Things System for Underground Mine Air Quality Pollutant Prediction Based on Azure Machine Learning.

PubMed

Jo, ByungWan; Khan, Rana Muhammad Asad

2018-03-21

The implementation of wireless sensor networks (WSNs) for monitoring the complex, dynamic, and harsh environment of underground coal mines (UCMs) is sought around the world to enhance safety. However, previously developed smart systems are limited to monitoring or, in a few cases, can report events. Therefore, this study introduces a reliable, efficient, and cost-effective internet of things (IoT) system for air quality monitoring with newly added features of assessment and pollutant prediction. This system is comprised of sensor modules, communication protocols, and a base station, running Azure Machine Learning (AML) Studio over it. Arduino-based sensor modules with eight different parameters were installed at separate locations of an operational UCM. Based on the sensed data, the proposed system assesses mine air quality in terms of the mine environment index (MEI). Principal component analysis (PCA) identified CH₄, CO, SO₂, and H₂S as the most influencing gases significantly affecting mine air quality. The results of PCA were fed into the ANN model in AML studio, which enabled the prediction of MEI. An optimum number of neurons were determined for both actual input and PCA-based input parameters. The results showed a better performance of the PCA-based ANN for MEI prediction, with R ² and RMSE values of 0.6654 and 0.2104, respectively. Therefore, the proposed Arduino and AML-based system enhances mine environmental safety by quickly assessing and predicting mine air quality.

Urban Landscape Characterization Using Remote Sensing Data For Input into Air Quality Modeling

NASA Technical Reports Server (NTRS)

Quattrochi, Dale A.; Estes, Maurice G., Jr.; Crosson, William; Khan, Maudood

2005-01-01

The urban landscape is inherently complex and this complexity is not adequately captured in air quality models that are used to assess whether urban areas are in attainment of EPA air quality standards, particularly for ground level ozone. This inadequacy of air quality models to sufficiently respond to the heterogeneous nature of the urban landscape can impact how well these models predict ozone pollutant levels over metropolitan areas and ultimately, whether cities exceed EPA ozone air quality standards. We are exploring the utility of high-resolution remote sensing data and urban growth projections as improved inputs to meteorological and air quality models focusing on the Atlanta, Georgia metropolitan area as a case study. The National Land Cover Dataset at 30m resolution is being used as the land use/land cover input and aggregated to the 4km scale for the MM5 mesoscale meteorological model and the Community Multiscale Air Quality (CMAQ) modeling schemes. Use of these data have been found to better characterize low density/suburban development as compared with USGS 1 km land use/land cover data that have traditionally been used in modeling. Air quality prediction for future scenarios to 2030 is being facilitated by land use projections using a spatial growth model. Land use projections were developed using the 2030 Regional Transportation Plan developed by the Atlanta Regional Commission. This allows the State Environmental Protection agency to evaluate how these transportation plans will affect future air quality.

TESTS OF INDOOR AIR QUALITY SINKS

EPA Science Inventory

Experiments were conducted in a room-size test chamber to determine the sink effects of selected materials on indoor air concentrations of p-dichlorobenzene (PDCB). hese effects might alter pollutant behavior from that predicted using similar indoor air quality models, by reducin...

The Simulations of Wildland Fire Smoke PM25 in the NWS Air Quality Forecasting Systems

NASA Astrophysics Data System (ADS)

Huang, H. C.; Pan, L.; McQueen, J.; Lee, P.; ONeill, S. M.; Ruminski, M.; Shafran, P.; Huang, J.; Stajner, I.; Upadhayay, S.; Larkin, N. K.

2017-12-01

The increase of wildland fire intensity and frequency in the United States (U.S.) has led to property loss, human fatality, and poor air quality due to elevated particulate matters and surface ozone concentrations. The NOAA/National Weather Service (NWS) built the National Air Quality Forecast Capability (NAQFC) based on the U.S. Environmental Protection Agency (EPA) Community Multi-scale Air Quality (CMAQ) Modeling System driven by the NCEP North American Mesoscale Forecast System meteorology to provide ozone and fine particulate matter (PM2.5) forecast guidance publicly. State and local forecasters use the NWS air quality forecast guidance to issue air quality alerts in their area. The NAQFC PM2.5 predictions include emissions from anthropogenic and biogenic sources, as well as natural sources such as dust storms and wildland fires. The wildland fire emission inputs to the NAQFC is derived from the NOAA National Environmental Satellite, Data, and Information Service Hazard Mapping System fire and smoke detection product and the emission module of the U.S. Forest Service (USFS) BlueSky Smoke Modeling Framework. Wildland fires are unpredictable and can be ignited by natural causes such as lightning or be human-caused. It is extremely difficult to predict future occurrences and behavior of wildland fires, as is the available bio-fuel to be burned for real-time air quality predictions. Assumptions of future day's wildland fire behavior often have to be made from older observed wildland fire information. The comparisons between the NAQFC modeled PM2.5 and the EPA AirNow surface observation show that large errors in PM2.5 prediction can occur if fire smoke emissions are sometimes placed at the wrong location and/or time. A configuration of NAQFC CMAQ-system to re-run previous 24 hours, during which wildland fires were observed from satellites has been included recently. This study focuses on the effort performed to minimize the error in NAQFC PM2.5 predictions resulting from incorporating fire smoke emissions into the NAQFC from a recently updated newer version of USFS BlueSky system. This study will show how new approaches has improved the PM2.5 predictions at both nearby and downstream areas from fire sources. Furthermore, Environment and Climate Change Canada (ECCC) fire emissions data are being tested.

Developing a smartphone software package for predicting atmospheric pollutant concentrations at mobile locations

PubMed Central

Larkin, Andrew; Williams, David E.; Kile, Molly L.; Baird, William M.

2014-01-01

Background There is considerable evidence that exposure to air pollution is harmful to health. In the U.S., ambient air quality is monitored by Federal and State agencies for regulatory purposes. There are limited options, however, for people to access this data in real-time which hinders an individual's ability to manage their own risks. This paper describes a new software package that models environmental concentrations of fine particulate matter (PM2.5), coarse particulate matter (PM10), and ozone concentrations for the state of Oregon and calculates personal health risks at the smartphone's current location. Predicted air pollution risk levels can be displayed on mobile devices as interactive maps and graphs color-coded to coincide with EPA air quality index (AQI) categories. Users have the option of setting air quality warning levels via color-coded bars and were notified whenever warning levels were exceeded by predicted levels within 10 km. We validated the software using data from participants as well as from simulations which showed that the application was capable of identifying spatial and temporal air quality trends. This unique application provides a potential low-cost technology for reducing personal exposure to air pollution which can improve quality of life particularly for people with health conditions, such as asthma, that make them more susceptible to these hazards. PMID:26146409

NOAA's National Air Quality Predictions and Development of Aerosol and Atmospheric Composition Prediction Components for the Next Generation Global Prediction System

NASA Astrophysics Data System (ADS)

Stajner, I.; Hou, Y. T.; McQueen, J.; Lee, P.; Stein, A. F.; Tong, D.; Pan, L.; Huang, J.; Huang, H. C.; Upadhayay, S.

2016-12-01

NOAA provides operational air quality predictions using the National Air Quality Forecast Capability (NAQFC): ozone and wildfire smoke for the United States and airborne dust for the contiguous 48 states at http://airquality.weather.gov. NOAA's predictions of fine particulate matter (PM2.5) became publicly available in February 2016. Ozone and PM2.5 predictions are produced using a system that operationally links the Community Multiscale Air Quality (CMAQ) model with meteorological inputs from the North American mesoscale forecast Model (NAM). Smoke and dust predictions are provided using the Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model. Current NAQFC focus is on updating CMAQ to version 5.0.2, improving PM2.5 predictions, and updating emissions estimates, especially for NOx using recently observed trends. Wildfire smoke emissions from a newer version of the USFS BlueSky system are being included in a new configuration of the NAQFC NAM-CMAQ system, which is re-run for the previous 24 hours when the wildfires were observed from satellites, to better represent wildfire emissions prior to initiating predictions for the next 48 hours. In addition, NOAA is developing the Next Generation Global Prediction System (NGGPS) to represent the earth system for extended weather prediction. NGGPS will include a representation of atmospheric dynamics, physics, aerosols and atmospheric composition as well as coupling with ocean, wave, ice and land components. NGGPS is being developed with a broad community involvement, including community developed components and academic research to develop and test potential improvements for potentially inclusion in NGGPS. Several investigators at NOAA's research laboratories and in academia are working to improve the aerosol and gaseous chemistry representation for NGGPS, to develop and evaluate the representation of atmospheric composition, and to establish and improve the coupling with radiation and microphysics. Additional efforts may include the improved use of predicted atmospheric composition in assimilation of observations and the linkage of full global atmospheric composition predictions with national air quality predictions.

Utility of NCEP Operational and Emerging Meteorological Models for Driving Air Quality Prediction

NASA Astrophysics Data System (ADS)

McQueen, J.; Huang, J.; Huang, H. C.; Shafran, P.; Lee, P.; Pan, L.; Sleinkofer, A. M.; Stajner, I.; Upadhayay, S.; Tallapragada, V.

2017-12-01

Operational air quality predictions for the United States (U. S.) are provided at NOAA by the National Air Quality Forecasting Capability (NAQFC). NAQFC provides nationwide operational predictions of ozone and particulate matter twice per day (at 06 and 12 UTC cycles) at 12 km resolution and 1 hour time intervals through 48 hours and distributed at http://airquality.weather.gov. The NOAA National Centers for Environmental Prediction (NCEP) operational North American Mesoscale (NAM) 12 km weather prediction is used to drive the Community Multiscale Air Quality (CMAQ) model. In 2017, the NAM was upgraded in part to reduce a warm 2m temperature bias in Summer (V4). At the same time CMAQ was updated to V5.0.2. Both versions of the models were run in parallel for several months. Therefore the impact of improvements from the atmospheric chemistry model versus upgrades with the weather prediction model could be assessed. . Improvements to CMAQ were related to improvements to improvements in NAM 2 m temperature bias through increasing the opacity of clouds and reducing downward shortwave radiation resulted in reduced ozone photolysis. Higher resolution operational NWP models have recently been introduced as part of the NCEP modeling suite. These include the NAM CONUS Nest (3 km horizontal resolution) run four times per day through 60 hours and the High Resolution Rapid Refresh (HRRR, 3 km) run hourly out to 18 hours. In addition, NCEP with other NOAA labs has begun to develop and test the Next Generation Global Prediction System (NGGPS) based on the FV3 global model. This presentation also overviews recent developments with operational numerical weather prediction and evaluates the ability of these models for predicting low level temperatures, clouds and capturing boundary layer processes important for driving air quality prediction in complex terrain. The assessed meteorological model errors could help determine the magnitude of possible pollutant errors from CMAQ if used for driving meteorology. The NWP models will be evaluated against standard and mesonet fields averaged for various regions during the summer 2017. An evaluation of meteorological fields important to air quality modeling (eg: near surface winds, temperatures, moisture and boundary layer heights, cloud cover) will be reported on.

76 FR 14626 - Approval and Promulgation of Implementation Plans; Kentucky; 110(a)(1) and (2) Infrastructure...

Federal Register 2010, 2011, 2012, 2013, 2014

2011-03-17

... provide relevant data for the purpose of predicting the effect on ambient air quality of the 8-hour ozone... the 1997 8-Hour Ozone National Ambient Air Quality Standards AGENCY: Environmental Protection Agency... Act) for the 1997 8-hour ozone national ambient air quality standards (NAAQS). Section 110(a) of the...

Dynamic evaluation of the CMAQv5.0 modeling system: Assessing the model’s ability to simulate ozone changes due to NOx emission reductions

EPA Science Inventory

Regional air quality models are frequently used for regulatory applications to predict changes in air quality due to changes in emissions or changes in meteorology. Dynamic model evaluation is thus an important step in establishing credibility in the model predicted pollutant re...

Likelihood of achieving air quality targets under model uncertainties.

PubMed

Digar, Antara; Cohan, Daniel S; Cox, Dennis D; Kim, Byeong-Uk; Boylan, James W

2011-01-01

Regulatory attainment demonstrations in the United States typically apply a bright-line test to predict whether a control strategy is sufficient to attain an air quality standard. Photochemical models are the best tools available to project future pollutant levels and are a critical part of regulatory attainment demonstrations. However, because photochemical models are uncertain and future meteorology is unknowable, future pollutant levels cannot be predicted perfectly and attainment cannot be guaranteed. This paper introduces a computationally efficient methodology for estimating the likelihood that an emission control strategy will achieve an air quality objective in light of uncertainties in photochemical model input parameters (e.g., uncertain emission and reaction rates, deposition velocities, and boundary conditions). The method incorporates Monte Carlo simulations of a reduced form model representing pollutant-precursor response under parametric uncertainty to probabilistically predict the improvement in air quality due to emission control. The method is applied to recent 8-h ozone attainment modeling for Atlanta, Georgia, to assess the likelihood that additional controls would achieve fixed (well-defined) or flexible (due to meteorological variability and uncertain emission trends) targets of air pollution reduction. The results show that in certain instances ranking of the predicted effectiveness of control strategies may differ between probabilistic and deterministic analyses.

Air pollution exposure prediction approaches used in air pollution epidemiology studies.

PubMed

Özkaynak, Halûk; Baxter, Lisa K; Dionisio, Kathie L; Burke, Janet

2013-01-01

Epidemiological studies of the health effects of outdoor air pollution have traditionally relied upon surrogates of personal exposures, most commonly ambient concentration measurements from central-site monitors. However, this approach may introduce exposure prediction errors and misclassification of exposures for pollutants that are spatially heterogeneous, such as those associated with traffic emissions (e.g., carbon monoxide, elemental carbon, nitrogen oxides, and particulate matter). We review alternative air quality and human exposure metrics applied in recent air pollution health effect studies discussed during the International Society of Exposure Science 2011 conference in Baltimore, MD. Symposium presenters considered various alternative exposure metrics, including: central site or interpolated monitoring data, regional pollution levels predicted using the national scale Community Multiscale Air Quality model or from measurements combined with local-scale (AERMOD) air quality models, hybrid models that include satellite data, statistically blended modeling and measurement data, concentrations adjusted by home infiltration rates, and population-based human exposure model (Stochastic Human Exposure and Dose Simulation, and Air Pollutants Exposure models) predictions. These alternative exposure metrics were applied in epidemiological applications to health outcomes, including daily mortality and respiratory hospital admissions, daily hospital emergency department visits, daily myocardial infarctions, and daily adverse birth outcomes. This paper summarizes the research projects presented during the symposium, with full details of the work presented in individual papers in this journal issue.

Feedbacks between Air Pollution and Weather, Part 1: Effects on Weather

EPA Science Inventory

The meteorological predictions of fully coupled air-quality models running in “feedback” versus “nofeedback” simulations were compared against each other as part of Phase 2 of the Air Quality Model Evaluation International Initiative. The model simulations included a “no-feedback...

Evaluating Air-Quality Models: Review and Outlook.

NASA Astrophysics Data System (ADS)

Weil, J. C.; Sykes, R. I.; Venkatram, A.

1992-10-01

Over the past decade, much attention has been devoted to the evaluation of air-quality models with emphasis on model performance in predicting the high concentrations that are important in air-quality regulations. This paper stems from our belief that this practice needs to be expanded to 1) evaluate model physics and 2) deal with the large natural or stochastic variability in concentration. The variability is represented by the root-mean- square fluctuating concentration (c about the mean concentration (C) over an ensemble-a given set of meteorological, source, etc. conditions. Most air-quality models used in applications predict C, whereas observations are individual realizations drawn from an ensemble. For cC large residuals exist between predicted and observed concentrations, which confuse model evaluations.This paper addresses ways of evaluating model physics in light of the large c the focus is on elevated point-source models. Evaluation of model physics requires the separation of the mean model error-the difference between the predicted and observed C-from the natural variability. A residual analysis is shown to be an elective way of doing this. Several examples demonstrate the usefulness of residuals as well as correlation analyses and laboratory data in judging model physics.In general, c models and predictions of the probability distribution of the fluctuating concentration (c), (c, are in the developmental stage, with laboratory data playing an important role. Laboratory data from point-source plumes in a convection tank show that (c approximates a self-similar distribution along the plume center plane, a useful result in a residual analysis. At pmsent,there is one model-ARAP-that predicts C, c, and (c for point-source plumes. This model is more computationally demanding than other dispersion models (for C only) and must be demonstrated as a practical tool. However, it predicts an important quantity for applications- the uncertainty in the very high and infrequent concentrations. The uncertainty is large and is needed in evaluating operational performance and in predicting the attainment of air-quality standards.

PREDICTING THE RELATIVE IMPACTS OF URBAN DEVELOPMENT POLICIES AND ON-ROAD VEHICLE TECHNOLOGIES ON AIR QUALITY IN THE UNITED STATES: MODELING AND ANALYSIS OF A CASE STUDY IN AUSTIN, TEXAS

EPA Science Inventory

Urban development results in changes to land use and land cover and, consequently, to biogenic and anthropogenic emissions, meteorological processes, and processes such as dry deposition that influence future predictions of air quality. This study examines the impacts of alter...

IMPACT OF AN OZONE GENERATOR AIR CLEANER ON STYRENE CONCENTRATIONS IN AN INDOOR AIR QUALITY RESEARCH CHAMBER

EPA Science Inventory

The paper gives results of an investigation of the impact of an ozone generator air cleaner on vapor-phase styrene concentrations in a full-scale indoor air quality test chamber. The time history of the concentrations of styrene and ozone is well predicted by a simulation model u...

The Impact of a Potential Shale Gas Development in Germany and the United Kingdom on Local and Regional Air Quality

NASA Astrophysics Data System (ADS)

Weger, L.; Lupascu, A.; Cremonese, L.; Butler, T. M.

2017-12-01

Numerous countries in Europe that possess domestic shale gas reserves are considering exploiting this unconventional gas resource as part of their energy transition agenda. While natural gas generates less CO2 emissions upon combustion compared to coal or oil, making it attractive as a bridge in the transition from fossil fuels to renewables, production of shale gas leads to emissions of CH4 and air pollutants such as NOx, VOCs and PM. These gases in turn influence the climate as well as air quality. In this study, we investigate the impact of a potential shale gas development in Germany and the United Kingdom on local and regional air quality. This work builds on our previous study in which we constructed emissions scenarios based on shale gas utilization in these counties. In order to explore the influence of shale gas production on air quality, we investigate emissions predicted from our shale gas scenarios with the Weather Research and Forecasting model with chemistry (WRF-Chem) model. In order to do this, we first design a model set-up over Europe and evaluate its performance for the meteorological and chemical parameters. Subsequently we add shale gas emissions fluxes based on the scenarios over the area of the grid in which the shale gas activities are predicted to occur. Finally, we model these emissions and analyze the impact on air quality on both a local and regional scale. The aims of this work are to predict the range of adverse effects on air quality, highlight the importance of emissions control strategies in reducing air pollution, to promote further discussion, and to provide policy makers with information for decision making on a potential shale gas development in the two study countries.

Predicting Air Quality at First Ingress into Vehicles Visiting the International Space Station.

PubMed

Romoser, Amelia A; Scully, Robert R; Limero, Thomas F; De Vera, Vanessa; Cheng, Patti F; Hand, Jennifer J; James, John T; Ryder, Valerie E

2017-02-01

NASA regularly performs ground-based offgas tests (OGTs), which allow prediction of accumulated volatile pollutant concentrations at first entry on orbit, on whole modules and vehicles scheduled to connect to the International Space Station (ISS). These data guide crew safety operations and allow for estimation of ISS air revitalization systems impact from additional pollutant load. Since volatiles released from vehicle, module, and payload materials can affect crew health and performance, prediction of first ingress air quality is important. To assess whether toxicological risk is typically over or underpredicted, OGT and first ingress samples from 10 vehicles and modules were compared. Samples were analyzed by gas chromatography and gas chromatography-mass spectrometry. The rate of pollutant accumulation was extrapolated over time. Ratios of analytical values and Spacecraft Maximum Allowable Concentrations were used to predict total toxicity values (T-values) at first entry. Results were also compared by compound. Frequently overpredicted was 2-butanone (9/10), whereas propanal (6/10) and ethanol (8/10) were typically underpredicted, but T-values were not substantially affected. Ingress sample collection delay (estimated by octafluoropropane introduced from ISS atmosphere) and T-value prediction accuracy correlated well (R2 = 0.9008), highlighting the importance of immediate air sample collection and accounting for ISS air dilution. Importantly, T-value predictions were conservative 70% of the time. Results also suggest that T-values can be normalized to octafluoropropane levels to adjust for ISS air dilution at first ingress. Finally, OGT and ingress sampling has allowed small leaks in vehicle fluid systems to be recognized and addressed.Romoser AA, Scully RR, Limero TF, De Vera V, Cheng PF, Hand JJ, James JT, Ryder VE. Predicting air quality at first ingress into vehicles visiting the International Space Station. Aerosp Med Hum Perform. 2017; 88(2):104-113.

Study on the Influence of Building Materials on Indoor Pollutants and Pollution Sources

NASA Astrophysics Data System (ADS)

Wang, Yao

2018-01-01

The paper summarizes the achievements and problems of indoor air quality research at home and abroad. The pollutants and pollution sources in the room are analyzed systematically. The types of building materials and pollutants are also discussed. The physical and chemical properties and health effects of main pollutants were analyzed and studied. According to the principle of mass balance, the basic mathematical model of indoor air quality is established. Considering the release rate of pollutants and indoor ventilation, a mathematical model for predicting the concentration of indoor air pollutants is derived. The model can be used to analyze and describe the variation of pollutant concentration in indoor air, and to predict and calculate the concentration of pollutants in indoor air at a certain time. The results show that the mathematical model established in this study can be used to analyze and predict the variation law of pollutant concentration in indoor air. The evaluation model can be used to evaluate the impact of indoor air quality and evaluation of current situation. Especially in the process of building and interior decoration, through pre-evaluation, it can provide reliable design parameters for selecting building materials and determining ventilation volume.

Air Quality Response Modeling for Decision Support | Science ...

EPA Pesticide Factsheets

Air quality management relies on photochemical models to predict the responses of pollutant concentrations to changes in emissions. Such modeling is especially important for secondary pollutants such as ozone and fine particulate matter which vary nonlinearly with changes in emissions. Numerous techniques for probing pollutant-emission relationships within photochemical models have been developed and deployed for a variety of decision support applications. However, atmospheric response modeling remains complicated by the challenge of validating sensitivity results against observable data. This manuscript reviews the state of the science of atmospheric response modeling as well as efforts to characterize the accuracy and uncertainty of sensitivity results. The National Exposure Research Laboratory′s (NERL′s) Atmospheric Modeling and Analysis Division (AMAD) conducts research in support of EPA′s mission to protect human health and the environment. AMAD′s research program is engaged in developing and evaluating predictive atmospheric models on all spatial and temporal scales for forecasting the Nation′s air quality and for assessing changes in air quality and air pollutant exposures, as affected by changes in ecosystem management and regulatory decisions. AMAD is responsible for providing a sound scientific and technical basis for regulatory policies based on air quality models to improve ambient air quality. The models developed by AMAD are being use

Assessing air quality in Aksaray with time series analysis

NASA Astrophysics Data System (ADS)

Kadilar, Gamze Özel; Kadilar, Cem

2017-04-01

Sulphur dioxide (SO2) is a major air pollutant caused by the dominant usage of diesel, petrol and fuels by vehicles and industries. One of the most air-polluted city in Turkey is Aksaray. Hence, in this study, the level of SO2 is analyzed in Aksaray based on the database monitored at air quality monitoring station of Turkey. Seasonal Autoregressive Integrated Moving Average (SARIMA) approach is used to forecast the level of SO2 air quality parameter. The results indicate that the seasonal ARIMA model provides reliable and satisfactory predictions for the air quality parameters and expected to be an alternative tool for practical assessment and justification.

Predicting indoor pollutant concentrations, and applications to air quality management

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lorenzetti, David M.

Because most people spend more than 90% of their time indoors, predicting exposure to airborne pollutants requires models that incorporate the effect of buildings. Buildings affect the exposure of their occupants in a number of ways, both by design (for example, filters in ventilation systems remove particles) and incidentally (for example, sorption on walls can reduce peak concentrations, but prolong exposure to semivolatile organic compounds). Furthermore, building materials and occupant activities can generate pollutants. Indoor air quality depends not only on outdoor air quality, but also on the design, maintenance, and use of the building. For example, ''sick building'' symptomsmore » such as respiratory problems and headaches have been related to the presence of air-conditioning systems, to carpeting, to low ventilation rates, and to high occupant density (1). The physical processes of interest apply even in simple structures such as homes. Indoor air quality models simulate the processes, such as ventilation and filtration, that control pollutant concentrations in a building. Section 2 describes the modeling approach, and the important transport processes in buildings. Because advection usually dominates among the transport processes, Sections 3 and 4 describe methods for predicting airflows. The concluding section summarizes the application of these models.« less

Delay Discounting as an Index of Sustainable Behavior: Devaluation of Future Air Quality and Implications for Public Health.

PubMed

Berry, Meredith S; Nickerson, Norma P; Odum, Amy L

2017-09-01

Poor air quality and resulting annual deaths represent significant public health concerns. Recently, rapid delay discounting (the devaluation of future outcomes) of air quality has been considered a potential barrier for engaging in long term, sustainable behaviors that might help to reduce emissions (e.g., reducing private car use, societal support for clean air initiatives). Delay discounting has been shown to be predictive of real world behavior outside of laboratory settings, and therefore may offer an important framework beyond traditional variables thought to measure sustainable behavior such as importance of an environmental issue, or environmental attitudes/values, although more research is needed in this area. We examined relations between discounting of air quality, respiratory health, and monetary gains and losses. We also examined, relations between discounting and self-reported importance of air quality and respiratory health, and nature relatedness. Results showed rapid delay discounting of all outcomes across the time frames assessed, and significant positive correlations between delay discounting of air quality, respiratory health, and monetary outcomes. Steeper discounting of monetary outcomes relative to air quality and respiratory health outcomes was observed in the context of gains; however, no differences in discounting were observed across losses of monetary, air quality, and respiratory health. Replicating the sign effect, monetary outcomes were discounted more steeply than monetary losses. Importance of air quality, respiratory health and nature relatedness were significantly and positively correlated with one another, but not with degree of delay discounting of any outcome, demonstrating the need for more comprehensive measures that predict pro-environmental behaviors that might benefit individuals and public health over time. These results add to our understanding of decision-making, and demonstrate alarming rates of delay discounting of air quality and health. These results implicate a major public health concern and potential barriers to individual and societal behavior that reduce pollution and emissions for conservation of clean air.

Delay Discounting as an Index of Sustainable Behavior: Devaluation of Future Air Quality and Implications for Public Health

PubMed Central

Berry, Meredith S.; Nickerson, Norma P.; Odum, Amy L.

2017-01-01

Poor air quality and resulting annual deaths represent significant public health concerns. Recently, rapid delay discounting (the devaluation of future outcomes) of air quality has been considered a potential barrier for engaging in long term, sustainable behaviors that might help to reduce emissions (e.g., reducing private car use, societal support for clean air initiatives). Delay discounting has been shown to be predictive of real world behavior outside of laboratory settings, and therefore may offer an important framework beyond traditional variables thought to measure sustainable behavior such as importance of an environmental issue, or environmental attitudes/values, although more research is needed in this area. We examined relations between discounting of air quality, respiratory health, and monetary gains and losses. We also examined, relations between discounting and self-reported importance of air quality and respiratory health, and nature relatedness. Results showed rapid delay discounting of all outcomes across the time frames assessed, and significant positive correlations between delay discounting of air quality, respiratory health, and monetary outcomes. Steeper discounting of monetary outcomes relative to air quality and respiratory health outcomes was observed in the context of gains; however, no differences in discounting were observed across losses of monetary, air quality, and respiratory health. Replicating the sign effect, monetary outcomes were discounted more steeply than monetary losses. Importance of air quality, respiratory health and nature relatedness were significantly and positively correlated with one another, but not with degree of delay discounting of any outcome, demonstrating the need for more comprehensive measures that predict pro-environmental behaviors that might benefit individuals and public health over time. These results add to our understanding of decision-making, and demonstrate alarming rates of delay discounting of air quality and health. These results implicate a major public health concern and potential barriers to individual and societal behavior that reduce pollution and emissions for conservation of clean air. PMID:28862671

Multiple Sensitivity Testing for Regional Air Quality Model in summer 2014

NASA Astrophysics Data System (ADS)

Tang, Y.; Lee, P.; Pan, L.; Tong, D.; Kim, H. C.; Huang, M.; Wang, J.; McQueen, J.; Lu, C. H.; Artz, R. S.

2015-12-01

The NOAA Air Resources laboratory leads to improve the performance of the U.S. Air Quality Forecasting Capability (NAQFC). It is operational in NOAA National Centers for Environmental Prediction (NCEP) which focuses on predicting surface ozone and PM2.5. In order to improve its performance, we tested several approaches, including NOAA Environmental Modeling System Global Aerosol Component (NGAC) simulation derived ozone and aerosol lateral boundary conditions (LBC), bi-direction NH3 emission and HMS(Hazard Mapping System)-BlueSky emission with the latest U.S. EPA Community Multi-scale Air Quality model (CMAQ) version and the U.S EPA National Emission Inventory (NEI)-2011 anthropogenic emissions. The operational NAQFC uses static profiles for its lateral boundary condition (LBC), which does not impose severe issue for near-surface air quality prediction. However, its degraded performance for the upper layer (e.g. above 3km) is evident when comparing with aircraft measured ozone. NCEP's Global Forecast System (GFS) has tracer O3 prediction treated as 3-D prognostic variable (Moorthi and Iredell, 1998) after being initialized with Solar Backscatter Ultra Violet-2 (SBUV-2) satellite data. We applied that ozone LBC to the CMAQ's upper layers and yield more reasonable O3 prediction than that with static LBC comparing with the aircraft data in Discover-AQ Colorado campaign. NGAC's aerosol LBC also improved the PM2.5 prediction with more realistic background aerosols. The bi-direction NH3 emission used in CMAQ also help reduce the NH3 and nitrate under-prediction issue. During summer 2014, strong wildfires occurred in northwestern USA, and we used the US Forest Service's BlueSky fire emission with HMS fire counts to drive CMAQ and tested the difference of day-1 and day-2 fire emission estimation. Other related issues were also discussed.

Dynamic Monitoring of Cleanroom Fallout Using an Air Particle Counter

NASA Technical Reports Server (NTRS)

Perry, Radford

2011-01-01

The particle fallout limitations and periodic allocations for the James Webb Space Telescope are very stringent. Standard prediction methods are complicated by non-linearity and monitoring methods that are insufficiently responsive. A method for dynamically predicting the particle fallout in a cleanroom using air particle counter data was determined by numerical correlation. This method provides a simple linear correlation to both time and air quality, which can be monitored in real time. The summation of effects provides the program better understanding of the cleanliness and assists in the planning of future activities. Definition of fallout rates within a cleanroom during assembly and integration of contamination-sensitive hardware, such as the James Webb Space Telescope, is essential for budgeting purposes. Balancing the activity levels for assembly and test with the particle accumulation rate is paramount. The current approach to predicting particle fallout in a cleanroom assumes a constant air quality based on the rated class of a cleanroom, with adjustments for projected work or exposure times. Actual cleanroom class can also depend on the number of personnel present and the type of activities. A linear correlation of air quality and normalized particle fallout was determined numerically. An air particle counter (standard cleanroom equipment) can be used to monitor the air quality on a real-time basis and determine the "class" of the cleanroom (per FED-STD-209 or ISO-14644). The correlation function provides an area coverage coefficient per class-hour of exposure. The prediction of particle accumulations provides scheduling inputs for activity levels and cleanroom class requirements.

The Copernicus Atmosphere Monitoring Service: facilitating the prediction of air quality from global to local scales

NASA Astrophysics Data System (ADS)

Engelen, R. J.; Peuch, V. H.

2017-12-01

The European Copernicus Atmosphere Monitoring Service (CAMS) operationally provides daily forecasts of global atmospheric composition and regional air quality. The global forecasting system is using ECMWF's Integrated Forecasting System (IFS), which is used for numerical weather prediction and which has been extended with modules for atmospheric chemistry, aerosols and greenhouse gases. The regional forecasts are produced by an ensemble of seven operational European air quality models that take their boundary conditions from the global system and provide an ensemble median with ensemble spread as their main output. Both the global and regional forecasting systems are feeding their output into air quality models on a variety of scales in various parts of the world. We will introduce the CAMS service chain and provide illustrations of its use in downstream applications. Both the usage of the daily forecasts and the usage of global and regional reanalyses will be addressed.

A state of the art regarding urban air quality prediction models

NASA Astrophysics Data System (ADS)

Croitoru, Cristiana; Nastase, Ilinca

2018-02-01

Urban pollution represents an increasing risk to residents of urban regions, particularly in large, over-industrialized cities knowing that the traffic is responsible for more than 25% of air gaseous pollutants and dust particles. Air quality modelling plays an important role in addressing air pollution control and management approaches by providing guidelines for better and more efficient air quality forecasting, along with smart monitoring sensor networks. The advances in technology regarding simulations, forecasting and monitoring are part of the new smart cities which offers a healthy environment for their occupants.

The use of quantile regression to forecast higher than expected respiratory deaths in a daily time series: a study of New York City data 1987-2000.

PubMed

Soyiri, Ireneous N; Reidpath, Daniel D

2013-01-01

Forecasting higher than expected numbers of health events provides potentially valuable insights in its own right, and may contribute to health services management and syndromic surveillance. This study investigates the use of quantile regression to predict higher than expected respiratory deaths. Data taken from 70,830 deaths occurring in New York were used. Temporal, weather and air quality measures were fitted using quantile regression at the 90th-percentile with half the data (in-sample). Four QR models were fitted: an unconditional model predicting the 90th-percentile of deaths (Model 1), a seasonal/temporal (Model 2), a seasonal, temporal plus lags of weather and air quality (Model 3), and a seasonal, temporal model with 7-day moving averages of weather and air quality. Models were cross-validated with the out of sample data. Performance was measured as proportionate reduction in weighted sum of absolute deviations by a conditional, over unconditional models; i.e., the coefficient of determination (R1). The coefficient of determination showed an improvement over the unconditional model between 0.16 and 0.19. The greatest improvement in predictive and forecasting accuracy of daily mortality was associated with the inclusion of seasonal and temporal predictors (Model 2). No gains were made in the predictive models with the addition of weather and air quality predictors (Models 3 and 4). However, forecasting models that included weather and air quality predictors performed slightly better than the seasonal and temporal model alone (i.e., Model 3 > Model 4 > Model 2) This study provided a new approach to predict higher than expected numbers of respiratory related-deaths. The approach, while promising, has limitations and should be treated at this stage as a proof of concept.

The Use of Quantile Regression to Forecast Higher Than Expected Respiratory Deaths in a Daily Time Series: A Study of New York City Data 1987-2000

PubMed Central

Soyiri, Ireneous N.; Reidpath, Daniel D.

2013-01-01

Forecasting higher than expected numbers of health events provides potentially valuable insights in its own right, and may contribute to health services management and syndromic surveillance. This study investigates the use of quantile regression to predict higher than expected respiratory deaths. Data taken from 70,830 deaths occurring in New York were used. Temporal, weather and air quality measures were fitted using quantile regression at the 90th-percentile with half the data (in-sample). Four QR models were fitted: an unconditional model predicting the 90th-percentile of deaths (Model 1), a seasonal / temporal (Model 2), a seasonal, temporal plus lags of weather and air quality (Model 3), and a seasonal, temporal model with 7-day moving averages of weather and air quality. Models were cross-validated with the out of sample data. Performance was measured as proportionate reduction in weighted sum of absolute deviations by a conditional, over unconditional models; i.e., the coefficient of determination (R1). The coefficient of determination showed an improvement over the unconditional model between 0.16 and 0.19. The greatest improvement in predictive and forecasting accuracy of daily mortality was associated with the inclusion of seasonal and temporal predictors (Model 2). No gains were made in the predictive models with the addition of weather and air quality predictors (Models 3 and 4). However, forecasting models that included weather and air quality predictors performed slightly better than the seasonal and temporal model alone (i.e., Model 3 > Model 4 > Model 2) This study provided a new approach to predict higher than expected numbers of respiratory related-deaths. The approach, while promising, has limitations and should be treated at this stage as a proof of concept. PMID:24147122

A Review and Analysis of Remote Sensing Capability for Air Quality Measurements as a Potential Decision Support Tool Conducted by the NASA DEVELOP Program

NASA Technical Reports Server (NTRS)

Ross, A.; Richards, A.; Keith, K.; Frew, C.; Boseck, J.; Sutton, S.; Watts, C.; Rickman, D.

2007-01-01

This project focused on a comprehensive utilization of air quality model products as decision support tools (DST) needed for public health applications. A review of past and future air quality measurement methods and their uncertainty, along with the relationship of air quality to national and global public health, is vital. This project described current and future NASA satellite remote sensing and ground sensing capabilities and the potential for using these sensors to enhance the prediction, prevention, and control of public health effects that result from poor air quality. The qualitative uncertainty of current satellite remotely sensed air quality, the ground-based remotely sensed air quality, the air quality/public health model, and the decision making process is evaluated in this study. Current peer-reviewed literature suggests that remotely sensed air quality parameters correlate well with ground-based sensor data. A satellite remote-sensed and ground-sensed data complement is needed to enhance the models/tools used by policy makers for the protection of national and global public health communities

DYNAMIC EVALUATION OF REGIONAL AIR QUALITY MODELS: ASSESSING CHANGES TO O 3 STEMMING FROM CHANGES IN EMISSIONS AND METEOROLOGY

EPA Science Inventory

Regional-scale air quality models are used to estimate the response of air pollutants to potential emission control strategies as part of the decision-making process. Traditionally, the model predicted pollutant concentrations are evaluated for the “base case” to assess a model’s...

Solutions Network Formulation Report. NASA's Potential Contributions for Using Solar Ultraviolet Radiation in Conjunction with Photocatalysis for Urban Air Pollution Mitigation and Increasing Air Quality

NASA Technical Reports Server (NTRS)

Underwood, Lauren; Ryan, Robert E.

2007-01-01

This Candidate Solution is based on using NASA Earth science research on atmospheric ozone and aerosols data as a means to predict and evaluate the effectiveness of photocatalytically created surfaces (building materials like glass, tile and cement) for air pollution mitigation purposes. When these surfaces are exposed to near UV light, organic molecules, like air pollutants and smog precursors, will degrade into environmentally friendly compounds. U.S. EPA (Environmental Protection Agency) is responsible for forecasting daily air quality by using the Air Quality Index (AQI) that is provided by AIRNow. EPA is partnered with AIRNow and is responsible for calculating the AQI for five major air pollutants that are regulated by the Clean Air Act. In this Solution, UV irradiance data acquired from the satellite mission Aura and the OMI Surface UV algorithm will be used to help understand both the efficacy and efficiency of the photocatalytic decomposition process these surfaces facilitate, and their ability to reduce air pollutants. Prediction models that estimate photocatalytic function do not exist. NASA UV irradiance data will enable this capability, so that air quality agencies that are run by state and local officials can develop and implement programs that utilize photocatalysis for urban air pollution control and, enable them to make effective decisions about air pollution protection programs.

PERFORMANCE AND DIAGNOSTIC EVALUATION OF OZONE PREDICTIONS BY THE ETA-COMMUNITY MULTISCALE AIR QUALITY FORECAST SYSTEM DURING THE 2002 NEW ENGLAND AIR QUALITY STUDY

EPA Science Inventory

A real-time air quality forecasting system (Eta-CMAQ model suite) has been developed by linking the NCEP Eta model to the U.S. EPA CMAQ model. This work presents results from the application of the Eta-CMAQ modeling system for forecasting O₃ over the northeastern U.S d...

Identify the dominant variables to predict stream water temperature

NASA Astrophysics Data System (ADS)

Chien, H.; Flagler, J.

2016-12-01

Stream water temperature is a critical variable controlling water quality and the health of aquatic ecosystems. Accurate prediction of water temperature and the assessment of the impacts of environmental variables on water temperature variation are critical for water resources management, particularly in the context of water quality and aquatic ecosystem sustainability. The objective of this study is to measure stream water temperature and air temperature and to examine the importance of streamflow on stream water temperature prediction. The measured stream water temperature and air temperature will be used to test two hypotheses: 1) streamflow is a relatively more important factor than air temperature in regulating water temperature, and 2) by combining air temperature and streamflow data stream water temperature can be more accurately estimated. Water and air temperature data loggers are placed at two USGS stream gauge stations #01362357and #01362370, located in the upper Esopus Creek watershed in Phonecia, NY. The ARIMA (autoregressive integrated moving average) time series model is used to analyze the measured water temperature data, identify the dominant environmental variables, and predict the water temperature with identified dominant variable. The preliminary results show that streamflow is not a significant variable in predicting stream water temperature at both USGS gauge stations. Daily mean air temperature is sufficient to predict stream water temperature at this site scale.

Meteorological regimes for the classification of aerospace air quality predictions for NASA-Kennedy Space Center

NASA Technical Reports Server (NTRS)

Stephens, J. B.; Sloan, J. C.

1976-01-01

A method is described for developing a statistical air quality assessment for the launch of an aerospace vehicle from the Kennedy Space Center in terms of existing climatological data sets. The procedure can be refined as developing meteorological conditions are identified for use with the NASA-Marshall Space Flight Center Rocket Exhaust Effluent Diffusion (REED) description. Classical climatological regimes for the long range analysis can be narrowed as the synoptic and mesoscale structure is identified. Only broad synoptic regimes are identified at this stage of analysis. As the statistical data matrix is developed, synoptic regimes will be refined in terms of the resulting eigenvectors as applicable to aerospace air quality predictions.

Near-roadway monitoring of vehicle emissions as a function of mode of operation for light-duty vehicles.

PubMed

Wen, Dongqi; Zhai, Wenjuan; Xiang, Sheng; Hu, Zhice; Wei, Tongchuan; Noll, Kenneth E

2017-11-01

Determination of the effect of vehicle emissions on air quality near roadways is important because vehicles are a major source of air pollution. A near-roadway monitoring program was undertaken in Chicago between August 4 and October 30, 2014, to measure ultrafine particles, carbon dioxide, carbon monoxide, traffic volume and speed, and wind direction and speed. The objective of this study was to develop a method to relate short-term changes in traffic mode of operation to air quality near roadways using data averaged over 5-min intervals to provide a better understanding of the processes controlling air pollution concentrations near roadways. Three different types of data analysis are provided to demonstrate the type of results that can be obtained from a near-roadway sampling program based on 5-min measurements: (1) development of vehicle emission factors (EFs) for ultrafine particles as a function of vehicle mode of operation, (2) comparison of measured and modeled CO 2 concentrations, and (3) application of dispersion models to determine concentrations near roadways. EFs for ultrafine particles are developed that are a function of traffic volume and mode of operation (free flow and congestion) for light-duty vehicles (LDVs) under real-world conditions. Two air quality models-CALINE4 (California Line Source Dispersion Model, version 4) and AERMOD (American Meteorological Society/U.S. Environmental Protection Agency Regulatory Model)-are used to predict the ultrafine particulate concentrations near roadways for comparison with measured concentrations. When using CALINE4 to predict air quality levels in the mixing cell, changes in surface roughness and stability class have no effect on the predicted concentrations. However, when using AERMOD to predict air quality in the mixing cell, changes in surface roughness have a significant impact on the predicted concentrations. The paper provides emission factors (EFs) that are a function of traffic volume and mode of operation (free flow and congestion) for LDVs under real-world conditions. The good agreement between monitoring and modeling results indicates that high-resolution, simultaneous measurements of air quality and meteorological and traffic conditions can be used to determine real-world, fleet-wide vehicle EFs as a function of vehicle mode of operation under actual driving conditions.

Modelling and analysis of ozone concentration by artificial intelligent techniques for estimating air quality

NASA Astrophysics Data System (ADS)

Taylan, Osman

2017-02-01

High ozone concentration is an important cause of air pollution mainly due to its role in the greenhouse gas emission. Ozone is produced by photochemical processes which contain nitrogen oxides and volatile organic compounds in the lower atmospheric level. Therefore, monitoring and controlling the quality of air in the urban environment is very important due to the public health care. However, air quality prediction is a highly complex and non-linear process; usually several attributes have to be considered. Artificial intelligent (AI) techniques can be employed to monitor and evaluate the ozone concentration level. The aim of this study is to develop an Adaptive Neuro-Fuzzy inference approach (ANFIS) to determine the influence of peripheral factors on air quality and pollution which is an arising problem due to ozone level in Jeddah city. The concentration of ozone level was considered as a factor to predict the Air Quality (AQ) under the atmospheric conditions. Using Air Quality Standards of Saudi Arabia, ozone concentration level was modelled by employing certain factors such as; nitrogen oxide (NOx), atmospheric pressure, temperature, and relative humidity. Hence, an ANFIS model was developed to observe the ozone concentration level and the model performance was assessed by testing data obtained from the monitoring stations established by the General Authority of Meteorology and Environment Protection of Kingdom of Saudi Arabia. The outcomes of ANFIS model were re-assessed by fuzzy quality charts using quality specification and control limits based on US-EPA air quality standards. The results of present study show that the ANFIS model is a comprehensive approach for the estimation and assessment of ozone level and is a reliable approach to produce more genuine outcomes.

NOAA's National Air Quality Prediction and Development of Aerosol and Atmospheric Composition Prediction Components for NGGPS

NASA Astrophysics Data System (ADS)

Stajner, I.; McQueen, J.; Lee, P.; Stein, A. F.; Wilczak, J. M.; Upadhayay, S.; daSilva, A.; Lu, C. H.; Grell, G. A.; Pierce, R. B.

2017-12-01

NOAA's operational air quality predictions of ozone, fine particulate matter (PM2.5) and wildfire smoke over the United States and airborne dust over the contiguous 48 states are distributed at http://airquality.weather.gov. The National Air Quality Forecast Capability (NAQFC) providing these predictions was updated in June 2017. Ozone and PM2.5 predictions are now produced using the system linking the Community Multiscale Air Quality model (CMAQ) version 5.0.2 with meteorological inputs from the North American Mesoscale Forecast System (NAM) version 4. Predictions of PM2.5 include intermittent dust emissions and wildfire emissions from an updated version of BlueSky system. For the latter, the CMAQ system is initialized by rerunning it over the previous 24 hours to include wildfire emissions at the time when they were observed from the satellites. Post processing to reduce the bias in PM2.5 prediction was updated using the Kalman filter analog (KFAN) technique. Dust related aerosol species at the CMAQ domain lateral boundaries now come from the NEMS Global Aerosol Component (NGAC) v2 predictions. Further development of NAQFC includes testing of CMAQ predictions to 72 hours, Canadian fire emissions data from Environment and Climate Change Canada (ECCC) and the KFAN technique to reduce bias in ozone predictions. NOAA is developing the Next Generation Global Predictions System (NGGPS) with an aerosol and gaseous atmospheric composition component to improve and integrate aerosol and ozone predictions and evaluate their impacts on physics, data assimilation and weather prediction. Efforts are underway to improve cloud microphysics, investigate aerosol effects and include representations of atmospheric composition of varying complexity into NGGPS: from the operational ozone parameterization, GOCART aerosols, with simplified ozone chemistry, to CMAQ chemistry with aerosol modules. We will present progress on community building, planning and development of NGGPS.

Forecasting PM10 in metropolitan areas: Efficacy of neural networks.

PubMed

Fernando, H J S; Mammarella, M C; Grandoni, G; Fedele, P; Di Marco, R; Dimitrova, R; Hyde, P

2012-04-01

Deterministic photochemical air quality models are commonly used for regulatory management and planning of urban airsheds. These models are complex, computer intensive, and hence are prohibitively expensive for routine air quality predictions. Stochastic methods are becoming increasingly popular as an alternative, which relegate decision making to artificial intelligence based on Neural Networks that are made of artificial neurons or 'nodes' capable of 'learning through training' via historic data. A Neural Network was used to predict particulate matter concentration at a regulatory monitoring site in Phoenix, Arizona; its development, efficacy as a predictive tool and performance vis-à-vis a commonly used regulatory photochemical model are described in this paper. It is concluded that Neural Networks are much easier, quicker and economical to implement without compromising the accuracy of predictions. Neural Networks can be used to develop rapid air quality warning systems based on a network of automated monitoring stations. Copyright © 2011 Elsevier Ltd. All rights reserved.

How Will Air Quality Change in South Asia by 2050?

NASA Astrophysics Data System (ADS)

Kumar, Rajesh; Barth, Mary C.; Pfister, G. G.; Delle Monache, L.; Lamarque, J. F.; Archer-Nicholls, S.; Tilmes, S.; Ghude, S. D.; Wiedinmyer, C.; Naja, M.; Walters, S.

2018-02-01

Exposure to unhealthy air causes millions of premature deaths and damages crops sufficient to feed a large portion of the South Asian population every year. However, little is known about how future air quality in South Asia will respond to changing human activities. Here we examine the combined effect of changes in climate and air pollutant emissions projected by the Representative Concentration Pathways (RCP) 8.5 and RCP6.0 on air quality of South Asia in 2050 using a state-of-the-science Nested Regional Climate model with Chemistry (NRCM-Chem). RCP8.5 and RCP6.0 are selected to represent scenarios of highest and lowest air pollution in South Asia by 2050. NRCM-Chem shows the ability to capture observed key features of variability in meteorological parameters, ozone and related gases, and aerosols. NRCM-Chem results show that surface ozone and particulate matter of less than 2.5 μm in diameter will increase significantly by midcentury in South Asia under the RCP8.5 but remain similar to present day under RCP6.0. No RCP suggest an improvement in air pollution in South Asia by midcentury. Under RCP8.5, the frequency of air pollution events is predicted to increase by 20-120 days per year in 2050 compared to the present-day conditions, with particulate matter of less than 2.5 μm in diameter predicted to breach the World Health Organization ambient air quality guidelines on an almost daily basis in many parts of South Asia. These results indicate that while the RCP scenarios project a global improvement in air quality, they generally result in degrading air quality in South Asia.

Implementation of a WRF-CMAQ Air Quality Modeling System in Bogotá, Colombia

NASA Astrophysics Data System (ADS)

Nedbor-Gross, R.; Henderson, B. H.; Pachon, J. E.; Davis, J. R.; Baublitz, C. B.; Rincón, A.

2014-12-01

Due to a continuous economic growth Bogotá, Colombia has experienced air pollution issues in recent years. The local environmental authority has implemented several strategies to curb air pollution that have resulted in the decrease of PM10 concentrations since 2010. However, more activities are necessary in order to meet international air quality standards in the city. The University of Florida Air Quality and Climate group is collaborating with the Universidad de La Salle to prioritize regulatory strategies for Bogotá using air pollution simulations. To simulate pollution, we developed a modeling platform that combines the Weather Research and Forecasting Model (WRF), local emissions, and the Community Multi-scale Air Quality model (CMAQ). This platform is the first of its kind to be implemented in the megacity of Bogota, Colombia. The presentation will discuss development and evaluation of the air quality modeling system, highlight initial results characterizing photochemical conditions in Bogotá, and characterize air pollution under proposed regulatory strategies. The WRF model has been configured and applied to Bogotá, which resides in a tropical climate with complex mountainous topography. Developing the configuration included incorporation of local topography and land-use data, a physics sensitivity analysis, review, and systematic evaluation. The threshold, however, was set based on synthesis of model performance under less mountainous conditions. We will evaluate the impact that differences in autocorrelation contribute to the non-ideal performance. Air pollution predictions are currently under way. CMAQ has been configured with WRF meteorology, global boundary conditions from GEOS-Chem, and a locally produced emission inventory. Preliminary results from simulations show promising performance of CMAQ in Bogota. Anticipated results include a systematic performance evaluation of ozone and PM10, characterization of photochemical sensitivity, and air quality predictions under proposed regulatory scenarios.

APPLICATION OF BAYESIAN MONTE CARLO ANALYSIS TO A LAGRANGIAN PHOTOCHEMICAL AIR QUALITY MODEL. (R824792)

EPA Science Inventory

Uncertainties in ozone concentrations predicted with a Lagrangian photochemical air quality model have been estimated using Bayesian Monte Carlo (BMC) analysis. Bayesian Monte Carlo analysis provides a means of combining subjective "prior" uncertainty estimates developed ...

A diagnostic model for studying daytime urban air quality trends

NASA Technical Reports Server (NTRS)

Brewer, D. A.; Remsberg, E. E.; Woodbury, G. E.

1981-01-01

A single cell Eulerian photochemical air quality simulation model was developed and validated for selected days of the 1976 St. Louis Regional Air Pollution Study (RAPS) data sets; parameterizations of variables in the model and validation studies using the model are discussed. Good agreement was obtained between measured and modeled concentrations of NO, CO, and NO2 for all days simulated. The maximum concentration of O3 was also predicted well. Predicted species concentrations were relatively insensitive to small variations in CO and NOx emissions and to the concentrations of species which are entrained as the mixed layer rises.

Adaptive Sampling for Urban Air Quality through Participatory Sensing

PubMed Central

Zeng, Yuanyuan; Xiang, Kai

2017-01-01

Air pollution is one of the major problems of the modern world. The popularization and powerful functions of smartphone applications enable people to participate in urban sensing to better know about the air problems surrounding them. Data sampling is one of the most important problems that affect the sensing performance. In this paper, we propose an Adaptive Sampling Scheme for Urban Air Quality (AS-air) through participatory sensing. Firstly, we propose to find the pattern rules of air quality according to the historical data contributed by participants based on Apriori algorithm. Based on it, we predict the on-line air quality and use it to accelerate the learning process to choose and adapt the sampling parameter based on Q-learning. The evaluation results show that AS-air provides an energy-efficient sampling strategy, which is adaptive toward the varied outside air environment with good sampling efficiency. PMID:29099766

ADVANCEMENTS IN SOURCE-TO-DOSE ANALYSIS OF POPULATION EXPOSURES TO OZONE

EPA Science Inventory

The current study takes advantage of the observations from regional air quality monitoring networks, the data from the NE-OPS (North East Oxidant and Particulate Study) Project in the Philadelphia region, and regional photochemical air quality model predictions to obtain and co...

Pragmatic estimation of a spatio-temporal air quality model with irregular monitoring data

NASA Astrophysics Data System (ADS)

Sampson, Paul D.; Szpiro, Adam A.; Sheppard, Lianne; Lindström, Johan; Kaufman, Joel D.

2011-11-01

Statistical analyses of health effects of air pollution have increasingly used GIS-based covariates for prediction of ambient air quality in "land use" regression models. More recently these spatial regression models have accounted for spatial correlation structure in combining monitoring data with land use covariates. We present a flexible spatio-temporal modeling framework and pragmatic, multi-step estimation procedure that accommodates essentially arbitrary patterns of missing data with respect to an ideally complete space by time matrix of observations on a network of monitoring sites. The methodology incorporates a model for smooth temporal trends with coefficients varying in space according to Partial Least Squares regressions on a large set of geographic covariates and nonstationary modeling of spatio-temporal residuals from these regressions. This work was developed to provide spatial point predictions of PM 2.5 concentrations for the Multi-Ethnic Study of Atherosclerosis and Air Pollution (MESA Air) using irregular monitoring data derived from the AQS regulatory monitoring network and supplemental short-time scale monitoring campaigns conducted to better predict intra-urban variation in air quality. We demonstrate the interpretation and accuracy of this methodology in modeling data from 2000 through 2006 in six U.S. metropolitan areas and establish a basis for likelihood-based estimation.

A theoretical framework for the episodic-urban air quality management plan ( e-UAQMP)

NASA Astrophysics Data System (ADS)

Gokhale, Sharad; Khare, Mukesh

The present research proposes the local urban air quality management plan which combines two different modelling approaches (hybrid model) and possesses an improved predictive ability including the 'probabilistic exceedances over norms' and their 'frequency of occurrences' and so termed, herein, as episodic-urban air quality management plan ( e-UAQMP). The e-UAQMP deals with the consequences of 'extreme' concentrations of pollutant, mainly occurring at urban 'hotspots' e.g. traffic junctions, intersections and signalized roadways and are also influenced by complexities of traffic generated 'wake' effects. The e-UAQMP (based on probabilistic approach), also acts as an efficient preventive measure to predict the 'probability of exceedances' so as to prepare a successful policy responses in relation to the protection of urban environment as well as disseminating information to its sensitive 'receptors'. The e-UAQMP may be tailored to the requirements of the local area for the policy implementation programmes. The importance of such policy-making framework in the context of current air pollution 'episodes' in urban environments is discussed. The hybrid model that is based on both deterministic and stochastic based approaches predicting the 'average' as well as 'extreme' concentration distribution of air pollutants together in form of probability has been used at two air quality control regions (AQCRs) in the Delhi city, India, in formulating and executing the e-UAQMP— first, the income tax office (ITO), one of the busiest signalized traffic intersection and second, the Sirifort, one of the busiest signalized roadways.

A site-specific screening comparison of modeled and monitored air dispersion and deposition for perfluorooctanoate.

PubMed

Barton, Catherine A; Zarzecki, Charles J; Russell, Mark H

2010-04-01

This work assessed the usefulness of a current air quality model (American Meteorological Society/Environmental Protection Agency Regulatory Model [AERMOD]) for predicting air concentrations and deposition of perfluorooctanoate (PFO) near a manufacturing facility. Air quality models play an important role in providing information for verifying permitting conditions and for exposure assessment purposes. It is important to ensure traditional modeling approaches are applicable to perfluorinated compounds, which are known to have unusual properties. Measured field data were compared with modeling predictions to show that AERMOD adequately located the maximum air concentration in the study area, provided representative or conservative air concentration estimates, and demonstrated bias and scatter not significantly different than that reported for other compounds. Surface soil/grass concentrations resulting from modeled deposition flux also showed acceptable bias and scatter compared with measured concentrations of PFO in soil/grass samples. Errors in predictions of air concentrations or deposition may be best explained by meteorological input uncertainty and conservatism in the PRIME algorithm used to account for building downwash. In general, AERMOD was found to be a useful screening tool for modeling the dispersion and deposition of PFO in air near a manufacturing facility.

SIMULATING REGIONAL-SCALE AIR QUALITY WITH DYNAMIC CHANGES IN REGIONAL CLIMATE AND CHEMICAL BOUNDARY CONDITIONS

EPA Science Inventory

This poster compares air quality modeling simulations under current climate and a future (approximately 2050) climate scenario. Differences in predicted ozone episodes and daily average PM_2.5 concentrations are presented, along with vertical ozone profiles. Modeling ...

Evaluation of an Air Quality Health Index for Predicting the Mutagenicity of Simulated Atmospheres

EPA Science Inventory

No study has evaluated the mutagenicity of atmospheres with a calculated air quality health index (AQHI). Thus, we generated in a UV-light-containing reaction chamber two simulated atmospheres (SAs) with similar AQHIs but different proportions of criteria pollutants and evaluated...

Rocket exhaust effluent modeling for tropospheric air quality and environmental assessments

NASA Technical Reports Server (NTRS)

Stephens, J. B.; Stewart, R. B.

1977-01-01

The various techniques for diffusion predictions to support air quality predictions and environmental assessments for aerospace applications are discussed in terms of limitations imposed by atmospheric data. This affords an introduction to the rationale behind the selection of the National Aeronautics and Space Administration (NASA)/Marshall Space Flight Center (MSFC) Rocket Exhaust Effluent Diffusion (REED) program. The models utilized in the NASA/MSFC REED program are explained. This program is then evaluated in terms of some results from a joint MSFC/Langley Research Center/Kennedy Space Center Titan Exhaust Effluent Prediction and Monitoring Program.

COMPARISON OF DATA FROM AN IAQ TEST HOUSE WITH PREDICTIONS OF AN IAQ COMPUTER MODEL

EPA Science Inventory

The paper describes several experiments to evaluate the impact of indoor air pollutant sources on indoor air quality (IAQ). Measured pollutant concentrations are compared with concentrations predicted by an IAQ model. The measured concentrations are in excellent agreement with th...

*Combining regional - and local-scale air quality models with exposure models for use in environmental health studies - Title changed from Linking air quality and exposure models for use in environmental health studies

EPA Science Inventory

Population-based human exposure models predict the distribution of personal exposures to pollutants of outdoor origin using a variety of inputs, including air pollution concentrations; human activity patterns, such as the amount of time spent outdoors versus indoors, commuting, w...

HISTORICAL EMISSION AND OZONE TRENDS IN THE HOUSTON AREA

EPA Science Inventory

An analysis of historical trend data for emissions and air quality in Houston for period of 1974-78 is conducted for the purposes of checking the EKMA O3-predicting model and of exploring empirical relations between emission changes and O3 air quality in the Houston area. Results...

Preparing the Model for Prediction Across Scales (MPAS) for global retrospective air quality modeling

EPA Science Inventory

The US EPA has a plan to leverage recent advances in meteorological modeling to develop a "Next-Generation" air quality modeling system that will allow consistent modeling of problems from global to local scale. The meteorological model of choice is the Model for Predic...

LINKING ETA MODEL WITH THE COMMUNITY MULTISCALE AIR QUALITY (CMAQ) MODELING SYSTEM: OZONE BOUNDARY CONDITIONS

EPA Science Inventory

A prototype surface ozone concentration forecasting model system for the Eastern U.S. has been developed. The model system is consisting of a regional meteorological and a regional air quality model. It demonstrated a strong prediction dependence on its ozone boundary conditions....

ASSESSMENT OF ETA-CMAQ FORECASTS OF PARTICULATE MATTER DISTRIBUTIONS THROUGH COMPARISONS WITH SURFACE NETWORK AND SPECIALIZED MEASUREMENTS

EPA Science Inventory

An air-quality forecasting (AQF) system based on the National Weather Service (NWS) National Centers for Environmental Prediction's (NCEP's) Eta model and the U.S. EPA's Community Multiscale Air Quality (CMAQ) Modeling System is used to simulate the distributions of tropospheric ...

Progress on Implementing Additional Physics Schemes into MPAS-A v5.1 for Next Generation Air Quality Modeling

EPA Science Inventory

The U.S. Environmental Protection Agency (USEPA) has a team of scientists developing a next generation air quality modeling system employing the Model for Prediction Across Scales – Atmosphere (MPAS-A) as its meteorological foundation. Several preferred physics schemes and ...

ORGANIC AEROSOL FORMATION IN THE HUMID, PHOTOCHEMICALLY-ACTIVE SOUTHEASTERN US: SOAS EXPERIMENTS AND SIMULATIONS

EPA Science Inventory

A better understanding of SOA formation, properties and behavior in the humid eastern U.S. including dependence on anthropogenic emissions (RFA Q #1, 2). More accurate air quality prediction enabling more accurate air quality management (EPA Goal #1). Scientific insights co...

The Application of Satellite-Derived, High-Resolution Land Use/Land Cover Data to Improve Urban Air Quality Model Forecasts

NASA Technical Reports Server (NTRS)

Quattrochi, D. A.; Lapenta, W. M.; Crosson, W. L.; Estes, M. G., Jr.; Limaye, A.; Kahn, M.

2006-01-01

Local and state agencies are responsible for developing state implementation plans to meet National Ambient Air Quality Standards. Numerical models used for this purpose simulate the transport and transformation of criteria pollutants and their precursors. The specification of land use/land cover (LULC) plays an important role in controlling modeled surface meteorology and emissions. NASA researchers have worked with partners and Atlanta stakeholders to incorporate an improved high-resolution LULC dataset for the Atlanta area within their modeling system and to assess meteorological and air quality impacts of Urban Heat Island (UHI) mitigation strategies. The new LULC dataset provides a more accurate representation of land use, has the potential to improve model accuracy, and facilitates prediction of LULC changes. Use of the new LULC dataset for two summertime episodes improved meteorological forecasts, with an existing daytime cold bias of approx. equal to 3 C reduced by 30%. Model performance for ozone prediction did not show improvement. In addition, LULC changes due to Atlanta area urbanization were predicted through 2030, for which model simulations predict higher urban air temperatures. The incorporation of UHI mitigation strategies partially offset this warming trend. The data and modeling methods used are generally applicable to other U.S. cities.

Investigation of the emissions and profiles of a wide range of VOCs during the Clean air for London project

NASA Astrophysics Data System (ADS)

Holmes, Rachel; Lidster, Richard; Hamilton, Jacqueline; Lee, James; Hopkins, James; Whalley, Lisa; Lewis, Alistair

2014-05-01

The majority of the World's population live in polluted urbanized areas. Poor air quality is shortening life expectancy of people in the UK by an average 7-8 months and costs society around £20 billion per year.[1] Despite this, our understanding of atmospheric processing in urban environments and its effect on air quality is incomplete. Air quality models are used to predict how air quality changes given different concentrations of pollution precursors, such as volatile organic compounds (VOCs). The urban environment of megacities pose a unique challenge for air quality measurements and modelling, due to high population densities, pollution levels and complex infrastructure. For over 60 years the air quality in London has been monitored, however the existing measurements are limited to a small group of compounds. In order to fully understand the chemical and physical processes that occur in London, more intensive and comprehensive measurements should be made. The Clean air for London (ClearfLo) project was conducted to investigate the air quality, in particular the boundary layer pollution, of London. A relatively new technique, comprehensive two dimensional gas chromatography (GC×GC) [2] was combined with a well-established dual channel GC (DC-GC) [3] system to provide a more comprehensive measurement of VOCs. A total of 78 individual VOCs (36 aliphatics, 19 monoaromatics, 21 oxygenated and 2 halogenated) and 10 groups of VOCs (8 aliphatic, 1 monoaromatic and 1 monoterpene) from C1-C13+ were quantified. Seasonal and diurnal profiles of these VOCs have been found which show the influence of emission source and chemical processing. Including these extra VOCs should enhance the prediction capability of air quality models thus informing policy makers on how to potentially improve air quality in megacities. References 1. House of Commons Environmental Audit Committee, Air Quality: A follow-up report, Ninth Report of session 2012-12. 2. Lidster, R.T., J.F. Hamilton, and A.C. Lewis, The application of two total transfer valve modulators for comprehensive two-dimensional gas chromatography of volatile organic compounds. Journal of Separation Science, 2011. 34(7): p. 812-821. 3. Hopkins, J.R., C.E. Jones, and A.C. Lewis, A dual channel gas chromatograph for atmospheric analysis of volatile organic compounds including oxygenated and monoterpene compounds. Journal of Environmental Monitoring, 2011. 13(8): p. 2268-2276.

Simulating smoke transport from wildland fires with a regional-scale air quality model: sensitivity to spatiotemporal allocation of fire emissions.

PubMed

Garcia-Menendez, Fernando; Hu, Yongtao; Odman, Mehmet T

2014-09-15

Air quality forecasts generated with chemical transport models can provide valuable information about the potential impacts of fires on pollutant levels. However, significant uncertainties are associated with fire-related emission estimates as well as their distribution on gridded modeling domains. In this study, we explore the sensitivity of fine particulate matter concentrations predicted by a regional-scale air quality model to the spatial and temporal allocation of fire emissions. The assessment was completed by simulating a fire-related smoke episode in which air quality throughout the Atlanta metropolitan area was affected on February 28, 2007. Sensitivity analyses were carried out to evaluate the significance of emission distribution among the model's vertical layers, along the horizontal plane, and into hourly inputs. Predicted PM2.5 concentrations were highly sensitive to emission injection altitude relative to planetary boundary layer height. Simulations were also responsive to the horizontal allocation of fire emissions and their distribution into single or multiple grid cells. Additionally, modeled concentrations were greatly sensitive to the temporal distribution of fire-related emissions. The analyses demonstrate that, in addition to adequate estimates of emitted mass, successfully modeling the impacts of fires on air quality depends on an accurate spatiotemporal allocation of emissions. Copyright © 2014 Elsevier B.V. All rights reserved.

Improvement of PM concentration predictability using WRF-CMAQ-DLM coupled system and its applications

NASA Astrophysics Data System (ADS)

Lee, Soon Hwan; Kim, Ji Sun; Lee, Kang Yeol; Shon, Keon Tae

2017-04-01

Air quality due to increasing Particulate Matter(PM) in Korea in Asia is getting worse. At present, the PM forecast is announced based on the PM concentration predicted from the air quality prediction numerical model. However, forecast accuracy is not as high as expected due to various uncertainties for PM physical and chemical characteristics. The purpose of this study was to develop a numerical-statistically ensemble models to improve the accuracy of prediction of PM10 concentration. Numerical models used in this study are the three dimensional atmospheric model Weather Research and Forecasting(WRF) and the community multiscale air quality model (CMAQ). The target areas for the PM forecast are Seoul, Busan, Daegu, and Daejeon metropolitan areas in Korea. The data used in the model development are PM concentration and CMAQ predictions and the data period is 3 months (March 1 - May 31, 2014). The dynamic-statistical technics for reducing the systematic error of the CMAQ predictions was applied to the dynamic linear model(DLM) based on the Baysian Kalman filter technic. As a result of applying the metrics generated from the dynamic linear model to the forecasting of PM concentrations accuracy was improved. Especially, at the high PM concentration where the damage is relatively large, excellent improvement results are shown.

Prediction of harmful water quality parameters combining weather, air quality and ecosystem models with in situ measurement

EPA Science Inventory

The ability to predict water quality in lakes is important since lakes are sources of water for agriculture, drinking, and recreational uses. Lakes are also home to a dynamic ecosystem of lacustrine wetlands and deep waters. They are sensitive to pH changes and are dependent on d...

Evaluation of near surface ozone and particulate matter in air ...

EPA Pesticide Factsheets

In this study, techniques typically used for future air quality projections are applied to a historical 11-year period to assess the performance of the modeling system when the driving meteorological conditions are obtained using dynamical downscaling of coarse-scale fields without correcting toward higher-resolution observations. The Weather Research and Forecasting model and the Community Multiscale Air Quality model are used to simulate regional climate and air quality over the contiguous United States for 2000–2010. The air quality simulations for that historical period are then compared to observations from four national networks. Comparisons are drawn between defined performance metrics and other published modeling results for predicted ozone, fine particulate matter, and speciated fine particulate matter. The results indicate that the historical air quality simulations driven by dynamically downscaled meteorology are typically within defined modeling performance benchmarks and are consistent with results from other published modeling studies using finer-resolution meteorology. This indicates that the regional climate and air quality modeling framework utilized here does not introduce substantial bias, which provides confidence in the method’s use for future air quality projections. This paper shows that if emissions inputs and coarse-scale meteorological inputs are reasonably accurate, then air quality can be simulated with acceptable accuracy even wi

Towards an operational high-resolution air quality forecasting system at ECCC

NASA Astrophysics Data System (ADS)

Munoz-Alpizar, Rodrigo; Stroud, Craig; Ren, Shuzhan; Belair, Stephane; Leroyer, Sylvie; Souvanlasy, Vanh; Spacek, Lubos; Pavlovic, Radenko; Davignon, Didier; Moran, Moran

2017-04-01

Urban environments are particularly sensitive to weather, air quality (AQ), and climatic conditions. Despite the efforts made in Canada to reduce pollution in urban areas, AQ continues to be a concern for the population, especially during short-term episodes that could lead to exceedances of daily air quality standards. Furthermore, urban air pollution has long been associated with significant adverse health effects. In Canada, the large percentage of the population living in urban areas ( 81%, according to the Canada's 2011 census) is exposed to elevated air pollution due to local emissions sources. Thus, in order to improve the services offered to the Canadian public, Environment and Climate Change Canada has launched an initiative to develop a high-resolution air quality prediction capacity for urban areas in Canada. This presentation will show observed pollution trends (2010-2016) for Canadian mega-cities along with some preliminary high-resolution air quality modelling results. Short-term and long-term plans for urban AQ forecasting in Canada will also be described.

A novel air quality analysis and prediction system for São Paulo, Brazil to support decision-making

NASA Astrophysics Data System (ADS)

Hoshyaripour, Gholam Ali; Brasseur, Guy; Andrade, Maria Fatima; Gavidia-Calderón, Mario; Bouarar, Idir

2016-04-01

The extensive economic development and urbanization in southeastern Brazil (SEB) in recent decades have notably degraded the air quality with adverse impacts on human health. Since the Metropolitan Area of São Paulo (MASP) accommodates the majority of the economic growth in SEB, it overwhelmingly suffers from the air pollution. Consequently, there is a strong demand for developing ever-better assessment mechanisms to monitor the air quality and to assist the decision makers to mitigate the air pollution in MASP. Here we present the results of an air quality modeling system designed for SEB with focuses on MASP. The Weather Research and Forecast model with Chemistry (WRF-Chem) is used considering the anthropogenic, biomass-burning and biogenic emissions within a 1000×1500 km domain with resolution of 10 km. FINN and MEGAN are used for the biomass-burning and biogenic emissions, respectively. For the anthropogenic emissions we use a local bottom-up inventory for the transport sector and the HTAPv2 global inventory for all other sectors. The bottom-up inventory accounts for the traffic patterns, vehicle types and their emission factors in the area and thus could be used to evaluate the effect of changes in these parameters on air quality in MASP. The model outputs are compered to the satellite and ground-based observations for O3 and NOx. The results show that using the bottom-up or top-down inventories individually can result in a huge deviation between the predictions and observations. On the other hand, combining the inventories significantly enhances the forecast accuracy. It also provides a powerful tool to quantify the effects of traffic and vehicle emission policies on air quality in MASP.

Air pollution dispersion models for human exposure predictions in London.

PubMed

Beevers, Sean D; Kitwiroon, Nutthida; Williams, Martin L; Kelly, Frank J; Ross Anderson, H; Carslaw, David C

2013-01-01

The London household survey has shown that people travel and are exposed to air pollutants differently. This argues for human exposure to be based upon space-time-activity data and spatio-temporal air quality predictions. For the latter, we have demonstrated the role that dispersion models can play by using two complimentary models, KCLurban, which gives source apportionment information, and Community Multi-scale Air Quality Model (CMAQ)-urban, which predicts hourly air quality. The KCLurban model is in close agreement with observations of NO(X), NO(2) and particulate matter (PM)(10/2.5), having a small normalised mean bias (-6% to 4%) and a large Index of Agreement (0.71-0.88). The temporal trends of NO(X) from the CMAQ-urban model are also in reasonable agreement with observations. Spatially, NO(2) predictions show that within 10's of metres of major roads, concentrations can range from approximately 10-20 p.p.b. up to 70 p.p.b. and that for PM(10/2.5) central London roadside concentrations are approximately double the suburban background concentrations. Exposure to different PM sources is important and we predict that brake wear-related PM(10) concentrations are approximately eight times greater near major roads than at suburban background locations. Temporally, we have shown that average NO(X) concentrations close to roads can range by a factor of approximately six between the early morning minimum and morning rush hour maximum periods. These results present strong arguments for the hybrid exposure model under development at King's and, in future, for in-building models and a model for the London Underground.

Predicting the effects of nanoscale cerium additives in diesel fuel on regional-scale air quality.

PubMed

Erdakos, Garnet B; Bhave, Prakash V; Pouliot, George A; Simon, Heather; Mathur, Rohit

2014-11-04

Diesel vehicles are a major source of air pollutant emissions. Fuel additives containing nanoparticulate cerium (nCe) are currently being used in some diesel vehicles to improve fuel efficiency. These fuel additives also reduce fine particulate matter (PM2.5) emissions and alter the emissions of carbon monoxide (CO), nitrogen oxides (NOx), and hydrocarbon (HC) species, including several hazardous air pollutants (HAPs). To predict their net effect on regional air quality, we review the emissions literature and develop a multipollutant inventory for a hypothetical scenario in which nCe additives are used in all on-road and nonroad diesel vehicles. We apply the Community Multiscale Air Quality (CMAQ) model to a domain covering the eastern U.S. for a summer and a winter period. Model calculations suggest modest decreases of average PM2.5 concentrations and relatively larger decreases in particulate elemental carbon. The nCe additives also have an effect on 8 h maximum ozone in summer. Variable effects on HAPs are predicted. The total U.S. emissions of fine-particulate cerium are estimated to increase 25-fold and result in elevated levels of airborne cerium (up to 22 ng/m3), which might adversely impact human health and the environment.

The statistical evaluation and comparison of ADMS-Urban model for the prediction of nitrogen dioxide with air quality monitoring network.

PubMed

Dėdelė, Audrius; Miškinytė, Auksė

2015-09-01

In many countries, road traffic is one of the main sources of air pollution associated with adverse effects on human health and environment. Nitrogen dioxide (NO2) is considered to be a measure of traffic-related air pollution, with concentrations tending to be higher near highways, along busy roads, and in the city centers, and the exceedances are mainly observed at measurement stations located close to traffic. In order to assess the air quality in the city and the air pollution impact on public health, air quality models are used. However, firstly, before the model can be used for these purposes, it is important to evaluate the accuracy of the dispersion modelling as one of the most widely used method. The monitoring and dispersion modelling are two components of air quality monitoring system (AQMS), in which statistical comparison was made in this research. The evaluation of the Atmospheric Dispersion Modelling System (ADMS-Urban) was made by comparing monthly modelled NO2 concentrations with the data of continuous air quality monitoring stations in Kaunas city. The statistical measures of model performance were calculated for annual and monthly concentrations of NO2 for each monitoring station site. The spatial analysis was made using geographic information systems (GIS). The calculation of statistical parameters indicated a good ADMS-Urban model performance for the prediction of NO2. The results of this study showed that the agreement of modelled values and observations was better for traffic monitoring stations compared to the background and residential stations.

Forecasting air quality time series using deep learning.

PubMed

Freeman, Brian S; Taylor, Graham; Gharabaghi, Bahram; Thé, Jesse

2018-04-13

This paper presents one of the first applications of deep learning (DL) techniques to predict air pollution time series. Air quality management relies extensively on time series data captured at air monitoring stations as the basis of identifying population exposure to airborne pollutants and determining compliance with local ambient air standards. In this paper, 8 hr averaged surface ozone (O 3 ) concentrations were predicted using deep learning consisting of a recurrent neural network (RNN) with long short-term memory (LSTM). Hourly air quality and meteorological data were used to train and forecast values up to 72 hours with low error rates. The LSTM was able to forecast the duration of continuous O 3 exceedances as well. Prior to training the network, the dataset was reviewed for missing data and outliers. Missing data were imputed using a novel technique that averaged gaps less than eight time steps with incremental steps based on first-order differences of neighboring time periods. Data were then used to train decision trees to evaluate input feature importance over different time prediction horizons. The number of features used to train the LSTM model was reduced from 25 features to 5 features, resulting in improved accuracy as measured by Mean Absolute Error (MAE). Parameter sensitivity analysis identified look-back nodes associated with the RNN proved to be a significant source of error if not aligned with the prediction horizon. Overall, MAE's less than 2 were calculated for predictions out to 72 hours. Novel deep learning techniques were used to train an 8-hour averaged ozone forecast model. Missing data and outliers within the captured data set were replaced using a new imputation method that generated calculated values closer to the expected value based on the time and season. Decision trees were used to identify input variables with the greatest importance. The methods presented in this paper allow air managers to forecast long range air pollution concentration while only monitoring key parameters and without transforming the data set in its entirety, thus allowing real time inputs and continuous prediction.

Metrics for the Evaluation the Utility of Air Quality Forecasting

NASA Astrophysics Data System (ADS)

Sumo, T. M.; Stockwell, W. R.

2013-12-01

Global warming is expected to lead to higher levels of air pollution and therefore the forecasting of both long-term and daily air quality is an important component for the assessment of the costs of climate change and its impact on human health. Some of the risks associated with poor air quality days (where the Air Pollution Index is greater than 100), include hospital visits and mortality. Accurate air quality forecasting has the potential to allow sensitive groups to take appropriate precautions. This research builds metrics for evaluating the utility of air quality forecasting in terms of its potential impacts. Our analysis of air quality models focuses on the Washington, DC/Baltimore, MD region over the summertime ozone seasons between 2010 and 2012. The metrics that are relevant to our analysis include: (1) The number of times that a high ozone or particulate matter (PM) episode is correctly forecasted, (2) the number of times that high ozone or PM episode is forecasted when it does not occur and (3) the number of times when the air quality forecast predicts a cleaner air episode when the air was observed to have high ozone or PM. Our evaluation of the performance of air quality forecasts include those forecasts of ozone and particulate matter and data available from the U.S. Environmental Protection Agency (EPA)'s AIRNOW. We also examined observational ozone and particulate matter data available from Clean Air Partners. Overall the forecast models perform well for our region and time interval.

Application of the Wind Erosion Prediction System in the AIRPACT regional air quality modeling framework

USDA-ARS?s Scientific Manuscript database

Wind erosion of soil is a major concern of the agricultural community as it removes the most fertile part of the soil and thus degrades soil productivity. Furthermore, dust emissions due to wind erosion contribute to poor air quality, reduce visibility, and cause perturbations to regional radiation ...

An Evaluation of carbon monoxide emissions models and mobile source dispersion models applicable to Alaskan cities : final report

DOT National Transportation Integrated Search

1986-01-01

This report describes an investigation of state-of-the-art models for predicting the impact on air quality of additions or changes to a highway system identified by the U.S. Environmental Protection Agency as a "non-attainment area" for air quality s...

The influence of ocean halogen and sulfur emissions in the air quality of a coastal megacity: The case of Los Angeles.

PubMed

Muñiz-Unamunzaga, Maria; Borge, Rafael; Sarwar, Golam; Gantt, Brett; de la Paz, David; Cuevas, Carlos A; Saiz-Lopez, Alfonso

2018-01-01

The oceans are the main source of natural halogen and sulfur compounds, which have a significant influence on the oxidizing capacity of the marine atmosphere; however, their impact on the air quality of coastal cities is currently unknown. We explore the effect of marine halogens (Cl, Br and I) and dimethyl sulfide (DMS) on the air quality of a large coastal city through a set of high-resolution (4-km) air quality simulations for the urban area of Los Angeles, US, using the Community Multiscale Air Quality (CMAQ model). The results indicate that marine halogen emissions decrease ozone and nitrogen dioxide levels up to 5ppbv and 2.5ppbv, respectively, in the city of Los Angeles. Previous studies suggested that the inclusion of chlorine in air quality models leads to the generation of ozone in urban areas through photolysis of nitryl chloride (ClNO 2 ). However, we find that when considering the chemistry of Cl, Br and I together the net effect is a reduction of surface ozone concentrations. Furthermore, combined ocean emissions of halogens and DMS cause substantial changes in the levels of key urban atmospheric oxidants such as OH, HO 2 and NO 3 , and in the composition and mass of fine particles. Although the levels of ozone, NO 3 and HO x are reduced, we find a 10% increase in secondary organic aerosol (SOA) mean concentration, attributed to the increase in aerosol acidity and sulfate aerosol formation when combining DMS and bromine. Therefore, this new pathway for enhanced SOA formation may potentially help with current model under predictions of urban SOA. Although further observations and research are needed to establish these preliminary conclusions, this first city-scale investigation suggests that the inclusion of oceanic halogens and DMS in air quality models may improve regional air quality predictions over coastal cities around the world. Copyright © 2017 Elsevier B.V. All rights reserved.

MODELING INDOOR CONCENTRATIONS AND EXPOSURE

EPA Science Inventory

The paper discusses the use of an indoor air quality model, EXPOSURE, to predict pollutant concentrations and exposures. The effects of indoor air pollutants depend on the concentrations of the pollutants and the exposure of individuals to the pollutants. The air pollutant concen...

TESTING U.S. EPA'S ISCST -VERSION 3 MODEL ON DIOXINS: A COMPARISON OF PREDICTED AND OBSERVED AIR AND SOIL CONCENTRATIONS

EPA Science Inventory

The central purpose of our study was to examine the performance of the United States Environmental Protection Agency's (EPA) nonreactive Gaussian air quality dispersion model, the Industrial Source Complex Short Term Model (ISCST3) Version 98226, in predicting polychlorinated dib...

Development and analysis of air quality modeling simulations for hazardous air pollutants

NASA Astrophysics Data System (ADS)

Luecken, D. J.; Hutzell, W. T.; Gipson, G. L.

The concentrations of five hazardous air pollutants were simulated using the community multi-scale air quality (CMAQ) modeling system. Annual simulations were performed over the continental United States for the entire year of 2001 to support human exposure estimates. Results are shown for formaldehyde, acetaldehyde, benzene, 1,3-butadiene and acrolein. Photochemical production in the atmosphere is predicted to dominate ambient formaldehyde and acetaldehyde concentrations, and to account for a significant fraction of ambient acrolein concentrations. Spatial and temporal variations are large throughout the domain over the year. Predicted concentrations are compared with observations for formaldehyde, acetaldehyde, benzene and 1,3-butadiene. Although the modeling results indicate an overall slight tendency towards underprediction, they reproduce episodic and seasonal behavior of pollutant concentrations at many monitors with good skill.

Local and regional interactions between air quality and climate in New Delhi- A sector based analysis

NASA Astrophysics Data System (ADS)

Marrapu, Pallavi

Deteriorating air quality is one of the major problems faced worldwide and in particular in Asia. The world's most polluted megacities are located in Asia highlighting the urgent need for efforts to improve the air quality. New Delhi (India), one of the world's most polluted cities, was the host of the Common Wealth Games during the period of 4-14 October 2010. This high profile event provided a good opportunity to accelerate efforts to improve air quality. Computational advances now allow air quality forecast models to fully couple the meteorology with chemical constituents within a unified modeling system that allows two-way interactions. The WRF-Chem model is used to simulate air quality in New Delhi. The thesis focuses on evaluating air quality and meteorology feedbacks. Four nested domains ranging from South Asia, Northern India, NCR Delhi and Delhi city at 45km, 15km, 5km and 1.67km resolution for a period of 20 day (26th Sep--15th Oct, 2010) are used in the study. The predicted mean surface concentrations of various pollutants show similar spatial distributions with peak values in the middle of the domain reflecting the traffic and population patterns in the city. Along with these activities, construction dust and industrial emissions contribute to high levels of criteria pollutants. The study evaluates the WRF-Chem capabilities using a new emission inventory developed over Delhi at a fine resolution of 1.67km and evaluating the results with observational data from 11 monitoring sties placed at various Game venues. The contribution of emission sectors including transportation, power, industry, and domestic to pollutant concentrations at targeted regions are studied and the results show that transportation and domestic sector are the major contributors to the pollution levels in Delhi, followed by industry. Apart from these sectors, emissions outside of Delhi contribute 20-50% to surface concentrations depending on the species. This indicates that pollution control efforts should take a regional perspective. Air quality projections in Delhi for 2030 are investigated. The Greenhouse Gas and Air Pollution I nteractions and Synergies (GAINS) model is used to generate a 2030 future emission scenario for Delhi using projections of air quality control measures and energy demands. Net reductions in CO concentrations by 50%, and increases of 140% and 40% in BC and NOx concentrations, respectively, are predicted. The net changes in concentration are associated with increases in transport and industry sectors. The domestic sector still has a significant contribution to air pollutant levels. The air quality levels show a profound effect under this scenario on the environment and human health. The increase in pollution from 2010 to 2030 is predicted to cause an increase in surface temperature by ˜0.65K. These increasing pollution levels also show effects on the radiative forcing. The high aerosols loading i.e. BC, PM2.5 and PM10 levels show strong influence on the short and longwave fluxes causing strong surface dimming and strong atmosphere heating due to BC. These results indicate transport and domestic sectors should be targeted for air quality and climate mitigations.

Climate impact on airborne particulate matter concentrations in California using seven year analysis periods

NASA Astrophysics Data System (ADS)

Mahmud, A.; Hixson, M.; Hu, J.; Zhao, Z.; Chen, S.-H.; Kleeman, M. J.

2010-11-01

The effect of global climate change on the annual average concentration of fine particulate matter (PM2.5) in California was studied using a climate-air quality modeling system composed of global through regional models. Output from the NCAR/DOE Parallel Climate Model (PCM) generated under the "business as usual" global emissions scenario was downscaled using the Weather Research and Forecasting (WRF) model followed by air quality simulations using the UCD/CIT airshed model. The system represents major atmospheric processes acting on gas and particle phase species including meteorological effects on emissions, advection, dispersion, chemical reaction rates, gas-particle conversion, and dry/wet deposition. The air quality simulations were carried out for the entire state of California with a resolution of 8-km for the years 2000-2006 (present climate with present emissions) and 2047-2053 (future climate with present emissions). Each of these 7-year analysis periods was analyzed using a total of 1008 simulated days to span a climatologically relevant time period with a practical computational burden. The 7-year windows were chosen to properly account for annual variability with the added benefit that the air quality predictions under the present climate could be compared to actual measurements. The climate-air quality modeling system successfully predicted the spatial pattern of present climate PM2.5 concentrations in California but the absolute magnitude of the annual average PM2.5 concentrations were under-predicted by ~4-39% in the major air basins. The majority of this under-prediction was caused by excess ventilation predicted by PCM-WRF that should be present to the same degree in the current and future time periods so that the net bias introduced into the comparison is minimized. Surface temperature, relative humidity (RH), rain rate, and wind speed were predicted to increase in the future climate while the ultra violet (UV) radiation was predicted to decrease in major urban areas in the San Joaquin Valley (SJV) and South Coast Air Basin (SoCAB). These changes lead to a predicted decrease in PM2.5 mass concentrations of ~0.3-0.7 μg m-3 in the southern portion of the SJV and ~0.3-1.1 μg m-3 along coastal regions of California including the heavily populated San Francisco Bay Area and the SoCAB surrounding Los Angeles. Annual average PM2.5 concentrations were predicted to increase at certain locations within the SJV and the Sacramento Valley (SV) due to the effects of climate change, but a corresponding analysis of the annual variability showed that these predictions are not statistically significant (i.e. the choice of a different 7-year period could produce a different outcome for these regions). Overall, virtually no region in California outside of coastal + central Los Angeles, and a small region around the port of Oakland in the San Francisco Bay Area experienced a statistically significant change in annual average PM2.5 concentrations due to the effects of climate change in the present~study. The present study employs the highest spatial resolution (8 km) and the longest analysis windows (7 years) of any climate-air quality analysis conducted for California to date, but the results still have some degree of uncertainty. Most significantly, GCM calculations have inherent uncertainty that is not fully represented in the current study since a single GCM was used as the starting point for all calculations. The PCM results used in the current study predicted greater wintertime increases in air temperature over the Pacific Ocean than over land, further motivating comparison to other GCM results. Ensembles of GCM results are usually employed to build confidence in climate calculations. The current results provide a first data-point for the climate-air quality analysis that simultaneously employ the fine spatial resolution and long time scales needed to capture the behavior of climate-PM2.5 interactions in California. Future downscaling studies should follow up with a full ensemble of GCMs as their starting point, and include aerosol feedback effects on local meteorology.

Potential assessment of the "support vector machine" method in forecasting ambient air pollutant trends.

PubMed

Lu, Wei-Zhen; Wang, Wen-Jian

2005-04-01

Monitoring and forecasting of air quality parameters are popular and important topics of atmospheric and environmental research today due to the health impact caused by exposing to air pollutants existing in urban air. The accurate models for air pollutant prediction are needed because such models would allow forecasting and diagnosing potential compliance or non-compliance in both short- and long-term aspects. Artificial neural networks (ANN) are regarded as reliable and cost-effective method to achieve such tasks and have produced some promising results to date. Although ANN has addressed more attentions to environmental researchers, its inherent drawbacks, e.g., local minima, over-fitting training, poor generalization performance, determination of the appropriate network architecture, etc., impede the practical application of ANN. Support vector machine (SVM), a novel type of learning machine based on statistical learning theory, can be used for regression and time series prediction and have been reported to perform well by some promising results. The work presented in this paper aims to examine the feasibility of applying SVM to predict air pollutant levels in advancing time series based on the monitored air pollutant database in Hong Kong downtown area. At the same time, the functional characteristics of SVM are investigated in the study. The experimental comparisons between the SVM model and the classical radial basis function (RBF) network demonstrate that the SVM is superior to the conventional RBF network in predicting air quality parameters with different time series and of better generalization performance than the RBF model.

MEGAPOLI: concept and first results of multi-scale modelling of megacity impacts

NASA Astrophysics Data System (ADS)

Baklanov, A. A.; Lawrence, M.; Pandis, S.

2009-09-01

The European FP7 project MEGAPOLI: ‘Megacities: Emissions, urban, regional and Global Atmospheric POLlution and climate effects, and Integrated tools for assessment and mitigation' (http://megapoli.info), started in October 2008, brings together 27 leading European research groups from 11 countries, state-of-the-art scientific tools and key players from countries outside Europe to investigate the interactions among megacities, air quality and climate. MEGAPOLI bridges the spatial and temporal scales that connect local emissions, air quality and weather with global atmospheric chemistry and climate. The main MEGAPOLI objectives are: 1. to assess impacts of megacities and large air-pollution hot-spots on local, regional and global air quality, 2. to quantify feedbacks among megacity air quality, local and regional climate, and global climate change, 3. to develop improved integrated tools for prediction of air pollution in megacities. In order to achieve these objectives the following tasks are realizing: • Develop and evaluate integrated methods to improve megacity emission data, • Investigate physical and chemical processes starting from the megacity street level, continuing to the city, regional and global scales, • Assess regional and global air quality impacts of megacity plumes, • Determine the main mechanisms of regional meteorology/climate forcing due to megacity plumes, • Assess global megacity pollutant forcing on climate, • Examine feedback mechanisms including effects of climate change on megacity air quality, • Develop integrated tools for prediction of megacity air quality, • Evaluate these integrated tools and use them in case studies, • Develop a methodology to estimate the impacts of different scenarios of megacity development on human health and climate change, • Propose and assess mitigation options to reduce the impacts of megacity emissions. We follow a pyramid strategy of undertaking detailed measurements in one European major city, Paris, performing detailed analysis for 12 megacities with existing air quality datasets and investigate the effects of all megacities on climate and global atmospheric chemistry. The project focuses on the multi-scale modelling of interacting meteorology and air quality, spanning the range from emissions to air quality, effects on climate, and feedbacks and mitigation potentials. Our hypothesis is that megacities around the world have an impact on air quality not only locally, but also regionally and globally and therefore can also influence the climate of our planet. Some of the links between megacities, air quality and climate are reasonably well-understood. However, a complete quantitative picture of these interactions is clearly missing. Understanding and quantifying these missing links is the focus of MEGAPOLI. The current status and modeling results after the first project year on examples of Paris and other European megacities are discussed.

An Overview of Atmospheric Chemistry and Air Quality Modeling

NASA Technical Reports Server (NTRS)

Johnson, Matthew S.

2017-01-01

This presentation will include my personal research experience and an overview of atmospheric chemistry and air quality modeling to the participants of the NASA Student Airborne Research Program (SARP 2017). The presentation will also provide examples on ways to apply airborne observations for chemical transport (CTM) and air quality (AQ) model evaluation. CTM and AQ models are important tools in understanding tropospheric-stratospheric composition, atmospheric chemistry processes, meteorology, and air quality. This presentation will focus on how NASA scientist currently apply CTM and AQ models to better understand these topics. Finally, the importance of airborne observation in evaluating these topics and how in situ and remote sensing observations can be used to evaluate and improve CTM and AQ model predictions will be highlighted.

The Fate of Trace Contaminants in a Crewed Spacecraft Cabin Environment

NASA Technical Reports Server (NTRS)

Perry, Jay L.; Kayatin, Matthew J.

2016-01-01

Trace chemical contaminants produced via equipment offgassing, human metabolic sources, and vehicle operations are removed from the cabin atmosphere by active contamination control equipment and incidental removal by other air quality control equipment. The fate of representative trace contaminants commonly observed in spacecraft cabin atmospheres is explored. Removal mechanisms are described and predictive mass balance techniques are reviewed. Results from the predictive techniques are compared to cabin air quality analysis results. Considerations are discussed for an integrated trace contaminant control architecture suitable for long duration crewed space exploration missions.

Air quality mapping using GIS and economic evaluation of health impact for Mumbai City, India.

PubMed

Kumar, Awkash; Gupta, Indrani; Brandt, Jørgen; Kumar, Rakesh; Dikshit, Anil Kumar; Patil, Rashmi S

2016-05-01

Mumbai, a highly populated city in India, has been selected for air quality mapping and assessment of health impact using monitored air quality data. Air quality monitoring networks in Mumbai are operated by National Environment Engineering Research Institute (NEERI), Maharashtra Pollution Control Board (MPCB), and Brihanmumbai Municipal Corporation (BMC). A monitoring station represents air quality at a particular location, while we need spatial variation for air quality management. Here, air quality monitored data of NEERI and BMC were spatially interpolated using various inbuilt interpolation techniques of ArcGIS. Inverse distance weighting (IDW), Kriging (spherical and Gaussian), and spline techniques have been applied for spatial interpolation for this study. The interpolated results of air pollutants sulfur dioxide (SO2), nitrogen dioxide (NO2) and suspended particulate matter (SPM) were compared with air quality data of MPCB in the same region. Comparison of results showed good agreement for predicted values using IDW and Kriging with observed data. Subsequently, health impact assessment of a ward was carried out based on total population of the ward and air quality monitored data within the ward. Finally, health cost within a ward was estimated on the basis of exposed population. This study helps to estimate the valuation of health damage due to air pollution. Operating more air quality monitoring stations for measurement of air quality is highly resource intensive in terms of time and cost. The appropriate spatial interpolation techniques can be used to estimate concentration where air quality monitoring stations are not available. Further, health impact assessment for the population of the city and estimation of economic cost of health damage due to ambient air quality can help to make rational control strategies for environmental management. The total health cost for Mumbai city for the year 2012, with a population of 12.4 million, was estimated as USD8000 million.

New smoke predictions for Alaska in NOAA’s National Air Quality Forecast Capability

NASA Astrophysics Data System (ADS)

Davidson, P. M.; Ruminski, M.; Draxler, R.; Kondragunta, S.; Zeng, J.; Rolph, G.; Stajner, I.; Manikin, G.

2009-12-01

Smoke from wildfire is an important component of fine particle pollution, which is responsible for tens of thousands of premature deaths each year in the US. In Alaska, wildfire smoke is the leading cause of poor air quality in summer. Smoke forecast guidance helps air quality forecasters and the public take steps to limit exposure to airborne particulate matter. A new smoke forecast guidance tool, built by a cross-NOAA team, leverages efforts of NOAA’s partners at the USFS on wildfire emissions information, and with EPA, in coordinating with state/local air quality forecasters. Required operational deployment criteria, in categories of objective verification, subjective feedback, and production readiness, have been demonstrated in experimental testing during 2008-2009, for addition to the operational products in NOAA's National Air Quality Forecast Capability. The Alaska smoke forecast tool is an adaptation of NOAA’s smoke predictions implemented operationally for the lower 48 states (CONUS) in 2007. The tool integrates satellite information on location of wildfires with weather (North American mesoscale model) and smoke dispersion (HYSPLIT) models to produce daily predictions of smoke transport for Alaska, in binary and graphical formats. Hour-by hour predictions at 12km grid resolution of smoke at the surface and in the column are provided each day by 13 UTC, extending through midnight next day. Forecast accuracy and reliability are monitored against benchmark criteria for accuracy and reliability. While wildfire activity in the CONUS is year-round, the intense wildfire activity in AK is limited to the summer. Initial experimental testing during summer 2008 was hindered by unusually limited wildfire activity and very cloudy conditions. In contrast, heavier than average wildfire activity during summer 2009 provided a representative basis (more than 60 days of wildfire smoke) for demonstrating required prediction accuracy. A new satellite observation product was developed for routine near-real time verification of these predictions. The footprint of the predicted smoke from identified fires is verified with satellite observations of the spatial extent of smoke aerosols (5km resolution). Based on geostationary aerosol optical depth measurements that provide good time resolution of the horizontal spatial extent of the plumes, these observations do not yield quantitative concentrations of smoke particles at the surface. Predicted surface smoke concentrations are consistent with the limited number of in situ observations of total fine particle mass from all sources; however they are much higher than predicted for most CONUS fires. To assess uncertainty associated with fire emissions estimates, sensitivity analyses are in progress.

Integration of Satellite, Modeled, and Ground Based Aerosol Data for use in Air Quality and Public Health Applications

NASA Astrophysics Data System (ADS)

Garcia, V.; Kondragunta, S.; Holland, D.; Dimmick, F.; Boothe, V.; Szykman, J.; Chu, A.; Kittaka, C.; Al-Saadi, J.; Engel-Cox, J.; Hoff, R.; Wayland, R.; Rao, S.; Remer, L.

2006-05-01

Advancements in remote sensing over the past decade have been recognized by governments around the world and led to the development of the international Global Earth Observation System of Systems 10-Year Implementation Plan. The plan for the U.S. contribution to GEOSS has been put forth in The Strategic Plan for the U.S. Integrated Earth Observation System (IEOS) developed under IWGEO-CENR. The approach for the development of the U.S. IEOS is to focus on specific societal benefits that can be achieved by integrating the nation's Earth observation capabilities. One such challenge is our ability to understand the impact of poor air quality on human health and well being. Historically, the air monitoring networks put in place for the Nations air quality programs provided the only aerosol air quality data on an ongoing and systematic basis at national levels. However, scientific advances in the remote sensing of aerosols from space have improved dramatically. The MODIS sensor and GOES Imager aboard NASA and NOAA satellites, respectively, provide synoptic-scale measurements of aerosol optical depth (AOD) which have been demonstrated to correlate with high levels of PM10 and PM2.5 at the surface. The MODIS sensor has been shown to be capable of a 1 km x 1 km (at nadir) AOD product, while the GOES Imager can provide AOD at 4 km x 4 km every 30 minutes. Within the next several years NOAA and EPA will begin to issue PM2.5 air quality forecasts over the entire domain of the eastern United States, eventually extending to national coverage. These forecasts will provide continuous estimated values of PM2.5 on a daily basis. A multi-agency collaborative project among government and academia is underway to improve the spatial prediction of fine particulate matter through the integration of multi-sensor and multi-platform aerosol observations (MODIS and GOES), numerical model output, and air monitoring data. By giving more weight to monitoring data in monitored areas and relying on adjusted model output and satellite data in non-monitored areas, a Bayesian hierarchical space-time model will be used to improve the accuracy of prediction and associated prediction errors. The improved spatial predictions will be tested as estimates of exposure for input to modeling relationships between air quality and asthma/other respiratory diseases through CDC under the Environmental Public Health Tracking Network. We will also focus on the use of the predictive spatial maps within the EPA AIRNow program which provides near real-time spatial maps of daily average PM2.5 concentrations across the US. We will present the overall project plan and preliminary results with emphasis on how GEOSS framework is facilitating this effort.

Seltzer_et_al_2016

EPA Pesticide Factsheets

This dataset supports the modeling study of Seltzer et al. (2016) published in Atmospheric Environment. In this study, techniques typically used for future air quality projections are applied to a historical 11-year period to assess the performance of the modeling system when the driving meteorological conditions are obtained using dynamical downscaling of coarse-scale fields without correcting toward higher resolution observations. The Weather Research and Forecasting model and the Community Multiscale Air Quality model are used to simulate regional climate and air quality over the contiguous United States for 2000-2010. The air quality simulations for that historical period are then compared to observations from four national networks. Comparisons are drawn between defined performance metrics and other published modeling results for predicted ozone, fine particulate matter, and speciated fine particulate matter. The results indicate that the historical air quality simulations driven by dynamically downscaled meteorology are typically within defined modeling performance benchmarks and are consistent with results from other published modeling studies using finer-resolution meteorology. This indicates that the regional climate and air quality modeling framework utilized here does not introduce substantial bias, which provides confidence in the method??s use for future air quality projections.This dataset is associated with the following publication:Seltzer, K., C

A Multi-Resolution Assessment of the Community Multiscale Air Quality (CMAQ) Model v4.7 Wet Deposition Estimates for 2002 - 2006

EPA Science Inventory

This paper examines the operational performance of the Community Multiscale Air Quality (CMAQ) model simulations for 2002 - 2006 using both 36-km and 12-km horizontal grid spacing, with a primary focus on the performance of the CMAQ model in predicting wet deposition of sulfate (...

EVALUATION OF THE COMMUNITY MULTISCALE AIR QUALITY (CMAQ) MODEL VERSION 4.5: UNCERTAINTIES AND SENSITIVITIES IMPACTING MODEL PERFORMANCE: PART I - OZONE

EPA Science Inventory

This study examines ozone (O₃) predictions from the Community Multiscale Air Quality (CMAQ) model version 4.5 and discusses potential factors influencing the model results. Daily maximum 8-hr average O₃ levels are largely underpredicted when observed O_...

Downscaler Model for predicting daily air pollution

EPA Pesticide Factsheets

This model combines daily ozone and particulate matter monitoring and modeling data from across the U.S. to provide improved fine-scale estimates of air quality in communities and other specific locales.

Temporalization of Electric Generation Emissions for Improved Representation of Peak Air Quality Episodes

NASA Astrophysics Data System (ADS)

Farkas, C. M.; Moeller, M.; Carlton, A. G.

2013-12-01

Photochemical transport models routinely under predict peak air quality events. This deficiency may be due, in part, to inadequate temporalization of emissions from the electric generating sector. The National Emissions Inventory (NEI) reports emissions from Electric Generating Units (EGUs) by either Continuous Emission Monitors (CEMs) that report hourly values or as an annual total. The Sparse Matrix Operator Kernel Emissions preprocessor (SMOKE), used to prepare emissions data for modeling with the CMAQ air quality model, allocates annual emission totals throughout the year using specific monthly, weekly, and hourly weights according to standard classification code (SCC) and location. This approach represents average diurnal and seasonal patterns of electricity generation but does not capture spikes in emissions due to episodic use as with peaking units or due to extreme weather events. In this project we use a combination of state air quality permits, CEM data, and EPA emission factors to more accurately temporalize emissions of NOx, SO2 and particulate matter (PM) during the extensive heat wave of July and August 2006. Two CMAQ simulations are conducted; the first with the base NEI emissions and the second with improved temporalization, more representative of actual emissions during the heat wave. Predictions from both simulations are evaluated with O3 and PM measurement data from EPA's National Air Monitoring Stations (NAMS) and State and Local Air Monitoring Stations (SLAMS) during the heat wave, for which ambient concentrations of criteria pollutants were often above NAAQS. During periods of increased photochemistry and high pollutant concentrations, it is critical that emissions are most accurately represented in air quality models.

Remote Sensing Characterization of the Urban Landscape for Improvement of Air Quality Modeling

NASA Technical Reports Server (NTRS)

Quattrochi, Dale A.; Estes, Maurice G., Jr.; Khan, Maudood

2005-01-01

The urban landscape is inherently complex and this complexity is not adequately captured in air quality models, particularly the Community Multiscale Air Quality (CMAQ) model that is used to assess whether urban areas are in attainment of EPA air quality standards, primarily for ground level ozone. This inadequacy of the CMAQ model to sufficiently respond to the heterogeneous nature of the urban landscape can impact how well the model predicts ozone pollutant levels over metropolitan areas and ultimately, whether cities exceed EPA ozone air quality standards. We are exploring the utility of high-resolution remote sensing data and urban growth projections as improved inputs to the meteorology component of the CMAQ model focusing on the Atlanta, Georgia metropolitan area as a case study. These growth projections include "business as usual" and "smart growth" scenarios out to 2030. The growth projections illustrate the effects of employing urban heat island mitigation strategies, such as increasing tree canopy and albedo across the Atlanta metro area, in moderating ground-level ozone and air temperature, compared to "business as usual" simulations in which heat island mitigation strategies are not applied. The National Land Cover Dataset at 30m resolution is being used as the land use/land cover input and aggregated to the 4km scale for the MM5 mesoscale meteorological model and the (CMAQ) modeling schemes. Use of these data has been found to better characterize low densityhburban development as compared with USGS 1 km land use/land cover data that have traditionally been used in modeling. Air quality prediction for fiture scenarios to 2030 is being facilitated by land use projections using a spatial growth model. Land use projections were developed using the 2030 Regional Transportation Plan developed by the Atlanta Regional Commission, the regional planning agency for the area. This allows the state Environmental Protection agency to evaluate how these transportation plans will affect fbture air quality.

A spectral method for spatial downscaling | Science Inventory ...

EPA Pesticide Factsheets

Complex computer models play a crucial role in air quality research. These models are used to evaluate potential regulatory impacts of emission control strategies and to estimate air quality in areas without monitoring data. For both of these purposes, it is important to calibrate model output with monitoring data to adjust for model biases and improve spatial prediction. In this paper, we propose a new spectral method to study and exploit complex relationships between model output and monitoring data. Spectral methods allow us to estimate the relationship between model output and monitoring data separately at different spatial scales, and to use model output for prediction only at the appropriate scales. The proposed method is computationally efficient and can be implemented using standard software. We apply the method to compare Community Multiscale Air Quality (CMAQ) model output with ozone measurements in the United States in July, 2005. We find that CMAQ captures large-scale spatial trends, but has low correlation with the monitoring data at small spatial scales. The National Exposure Research Laboratory′s (NERL′s)Atmospheric Modeling Division (AMAD) conducts research in support of EPA′s mission to protect human health and the environment. AMAD′s research program is engaged in developing and evaluating predictive atmospheric models on all spatial and temporal scales for forecasting the Nation′s air quality and for assessing ch

Updating Sea Spray Aerosol Emissions in the Community Multiscale Air Quality Model

NASA Astrophysics Data System (ADS)

Gantt, B.; Bash, J. O.; Kelly, J.

2014-12-01

Sea spray aerosols (SSA) impact the particle mass concentration and gas-particle partitioning in coastal environments, with implications for human and ecosystem health. In this study, the Community Multiscale Air Quality (CMAQ) model is updated to enhance fine mode SSA emissions, include sea surface temperature (SST) dependency, and revise surf zone emissions. Based on evaluation with several regional and national observational datasets in the continental U.S., the updated emissions generally improve surface concentrations predictions of primary aerosols composed of sea-salt and secondary aerosols affected by sea-salt chemistry in coastal and near-coastal sites. Specifically, the updated emissions lead to better predictions of the magnitude and coastal-to-inland gradient of sodium, chloride, and nitrate concentrations at Bay Regional Atmospheric Chemistry Experiment (BRACE) sites near Tampa, FL. Including SST-dependency to the SSA emission parameterization leads to increased sodium concentrations in the southeast U.S. and decreased concentrations along the Pacific coast and northeastern U.S., bringing predictions into closer agreement with observations at most Interagency Monitoring of Protected Visual Environments (IMPROVE) and Chemical Speciation Network (CSN) sites. Model comparison with California Research at the Nexus of Air Quality and Climate Change (CalNex) observations will also be discussed, with particular focus on the South Coast Air Basin where clean marine air mixes with anthropogenic pollution in a complex environment. These SSA emission updates enable more realistic simulation of chemical processes in coastal environments, both in clean marine air masses and mixtures of clean marine and polluted conditions.

Prediction on carbon dioxide emissions based on fuzzy rules

NASA Astrophysics Data System (ADS)

Pauzi, Herrini; Abdullah, Lazim

2014-06-01

There are several ways to predict air quality, varying from simple regression to models based on artificial intelligence. Most of the conventional methods are not sufficiently able to provide good forecasting performances due to the problems with non-linearity uncertainty and complexity of the data. Artificial intelligence techniques are successfully used in modeling air quality in order to cope with the problems. This paper describes fuzzy inference system (FIS) to predict CO2 emissions in Malaysia. Furthermore, adaptive neuro-fuzzy inference system (ANFIS) is used to compare the prediction performance. Data of five variables: energy use, gross domestic product per capita, population density, combustible renewable and waste and CO2 intensity are employed in this comparative study. The results from the two model proposed are compared and it is clearly shown that the ANFIS outperforms FIS in CO2 prediction.

A changing climate: impacts on human exposures to O3 using ...

EPA Pesticide Factsheets

Predicting the impacts of changing climate on human exposure to air pollution requires future scenarios that account for changes in ambient pollutant concentrations, population sizes and distributions, and housing stocks. An integrated methodology to model changes in human exposures due to these impacts was developed by linking climate, air quality, land-use, and human exposure models. This methodology was then applied to characterize changes in predicted human exposures to O3 under multiple future scenarios. Regional climate projections for the U.S. were developed by downscaling global circulation model (GCM) scenarios for three of the Intergovernmental Panel on Climate Change’s (IPCC’s) Representative Concentration Pathways (RCPs) using the Weather Research and Forecasting (WRF) model. The regional climate results were in turn used to generate air quality (concentration) projections using the Community Multiscale Air Quality (CMAQ) model. For each of the climate change scenarios, future U.S. census-tract level population distributions from the Integrated Climate and Land Use Scenarios (ICLUS) model for four future scenarios based on the IPCC’s Special Report on Emissions Scenarios (SRES) storylines were used. These climate, air quality, and population projections were used as inputs to EPA’s Air Pollutants Exposure (APEX) model for 12 U.S. cities. Probability density functions show changes in the population distribution of 8 h maximum daily O3 exposur

Research and application of a novel hybrid air quality early-warning system: A case study in China.

PubMed

Li, Chen; Zhu, Zhijie

2018-06-01

As one of the most serious meteorological disasters in modern society, air pollution has received extensive attention from both citizens and decision-makers. With the complexity of pollution components and the uncertainty of prediction, it is both critical and challenging to construct an effective and practical early-warning system. In this paper, a novel hybrid air quality early-warning system for pollution contaminant monitoring and analysis was proposed. To improve the efficiency of the system, an advanced attribute selection method based on fuzzy evaluation and rough set theory was developed to select the main pollution contaminants for cities. Moreover, a hybrid model composed of the theory of "decomposition and ensemble", an extreme learning machine and an advanced heuristic algorithm was developed for pollution contaminant prediction; it provides deterministic and interval forecasting for tackling the uncertainty of future air quality. Daily pollution contaminants of six major cities in China were selected as a dataset to evaluate the practicality and effectiveness of the developed air quality early-warning system. The superior experimental performance determined by the values of several error indexes illustrated that the proposed early-warning system was of great effectiveness and efficiency. Copyright © 2018 Elsevier B.V. All rights reserved.

Can Elevated Air [CO2] Conditions Mitigate the Predicted Warming Impact on the Quality of Coffee Bean?

PubMed

Ramalho, José C; Pais, Isabel P; Leitão, António E; Guerra, Mauro; Reboredo, Fernando H; Máguas, Cristina M; Carvalho, Maria L; Scotti-Campos, Paula; Ribeiro-Barros, Ana I; Lidon, Fernando J C; DaMatta, Fábio M

2018-01-01

Climate changes, mostly related to high temperature, are predicted to have major negative impacts on coffee crop yield and bean quality. Recent studies revealed that elevated air [CO 2 ] mitigates the impact of heat on leaf physiology. However, the extent of the interaction between elevated air [CO 2 ] and heat on coffee bean quality was never addressed. In this study, the single and combined impacts of enhanced [CO 2 ] and temperature in beans of Coffea arabica cv. Icatu were evaluated. Plants were grown at 380 or 700 μL CO 2 L -1 air, and then submitted to a gradual temperature rise from 25°C up to 40°C during ca. 4 months. Fruits were harvested at 25°C, and in the ranges of 30-35 or 36-40°C, and bean physical and chemical attributes with potential implications on quality were then examined. These included: color, phenolic content, soluble solids, chlorogenic, caffeic and p -coumaric acids, caffeine, trigonelline, lipids, and minerals. Most of these parameters were mainly affected by temperature (although without a strong negative impact on bean quality), and only marginally, if at all, by elevated [CO 2 ]. However, the [CO 2 ] vs. temperature interaction strongly attenuated some of the negative impacts promoted by heat (e.g., total chlorogenic acids), thus maintaining the bean characteristics closer to those obtained under adequate temperature conditions (e.g., soluble solids, caffeic and p -coumaric acids, trigonelline, chroma, Hue angle, and color index), and increasing desirable features (acidity). Fatty acid and mineral pools remained quite stable, with only few modifications due to elevated air [CO 2 ] (e.g., phosphorous) and/or heat. In conclusion, exposure to high temperature in the last stages of fruit maturation did not strongly depreciate bean quality, under the conditions of unrestricted water supply and moderate irradiance. Furthermore, the superimposition of elevated air [CO 2 ] contributed to preserve bean quality by modifying and mitigating the heat impact on physical and chemical traits of coffee beans, which is clearly relevant in a context of predicted climate change and global warming scenarios.

Can Elevated Air [CO2] Conditions Mitigate the Predicted Warming Impact on the Quality of Coffee Bean?

PubMed Central

Ramalho, José C.; Pais, Isabel P.; Leitão, António E.; Guerra, Mauro; Reboredo, Fernando H.; Máguas, Cristina M.; Carvalho, Maria L.; Scotti-Campos, Paula; Ribeiro-Barros, Ana I.; Lidon, Fernando J. C.; DaMatta, Fábio M.

2018-01-01

Climate changes, mostly related to high temperature, are predicted to have major negative impacts on coffee crop yield and bean quality. Recent studies revealed that elevated air [CO2] mitigates the impact of heat on leaf physiology. However, the extent of the interaction between elevated air [CO2] and heat on coffee bean quality was never addressed. In this study, the single and combined impacts of enhanced [CO2] and temperature in beans of Coffea arabica cv. Icatu were evaluated. Plants were grown at 380 or 700 μL CO2 L-1 air, and then submitted to a gradual temperature rise from 25°C up to 40°C during ca. 4 months. Fruits were harvested at 25°C, and in the ranges of 30–35 or 36–40°C, and bean physical and chemical attributes with potential implications on quality were then examined. These included: color, phenolic content, soluble solids, chlorogenic, caffeic and p-coumaric acids, caffeine, trigonelline, lipids, and minerals. Most of these parameters were mainly affected by temperature (although without a strong negative impact on bean quality), and only marginally, if at all, by elevated [CO2]. However, the [CO2] vs. temperature interaction strongly attenuated some of the negative impacts promoted by heat (e.g., total chlorogenic acids), thus maintaining the bean characteristics closer to those obtained under adequate temperature conditions (e.g., soluble solids, caffeic and p-coumaric acids, trigonelline, chroma, Hue angle, and color index), and increasing desirable features (acidity). Fatty acid and mineral pools remained quite stable, with only few modifications due to elevated air [CO2] (e.g., phosphorous) and/or heat. In conclusion, exposure to high temperature in the last stages of fruit maturation did not strongly depreciate bean quality, under the conditions of unrestricted water supply and moderate irradiance. Furthermore, the superimposition of elevated air [CO2] contributed to preserve bean quality by modifying and mitigating the heat impact on physical and chemical traits of coffee beans, which is clearly relevant in a context of predicted climate change and global warming scenarios. PMID:29559990

New directions: Atmospheric chemical mechanisms for the future

EPA Science Inventory

The chemical reaction scheme or mechanism used to represent atmospheric chemical reactions is at the heart of each air quality model used in research and policy applications to predict and analyse the complex air pollutants: ozone, air toxics and PM2.5. This is necessarily only a...

Enhanced data validation strategy of air quality monitoring network.

PubMed

Harkat, Mohamed-Faouzi; Mansouri, Majdi; Nounou, Mohamed; Nounou, Hazem

2018-01-01

Quick validation and detection of faults in measured air quality data is a crucial step towards achieving the objectives of air quality networks. Therefore, the objectives of this paper are threefold: (i) to develop a modeling technique that can be used to predict the normal behavior of air quality variables and help provide accurate reference for monitoring purposes; (ii) to develop fault detection method that can effectively and quickly detect any anomalies in measured air quality data. For this purpose, a new fault detection method that is based on the combination of generalized likelihood ratio test (GLRT) and exponentially weighted moving average (EWMA) will be developed. GLRT is a well-known statistical fault detection method that relies on maximizing the detection probability for a given false alarm rate. In this paper, we propose to develop GLRT-based EWMA fault detection method that will be able to detect the changes in the values of certain air quality variables; (iii) to develop fault isolation and identification method that allows defining the fault source(s) in order to properly apply appropriate corrective actions. In this paper, reconstruction approach that is based on Midpoint-Radii Principal Component Analysis (MRPCA) model will be developed to handle the types of data and models associated with air quality monitoring networks. All air quality modeling, fault detection, fault isolation and reconstruction methods developed in this paper will be validated using real air quality data (such as particulate matter, ozone, nitrogen and carbon oxides measurement). Copyright © 2017 Elsevier Inc. All rights reserved.

Joint Conference on Sensing of Environmental Pollutants, 4th, New Orleans, La., November 6-11, 1977, Proceedings

NASA Technical Reports Server (NTRS)

1978-01-01

Papers are presented on such topics as environmental chemistry, the effects of sulfur compounds on air quality, the prediction and monitoring of biological effects caused by environmental pollutants, environmental indicators, the satellite remote sensing of air pollution, weather and climate modification by pollution, and the monitoring and assessment of radioactive pollutants. Consideration is also given to empirical and quantitative modeling of air quality, disposal of hazardous and nontoxic materials, sensing and assessment of water quality, pollution source monitoring, and assessment of some environmental impacts of fossil and nuclear fuels.

“Nitrogen Budgets for the Mississippi River Basin using the ...

EPA Pesticide Factsheets

Presentation on the results from the 3 linked models, EPIC (USDA), CMAQ and NEWS to analyze a scenario of increased corn production related to biofuels together with Clean Air Act emission reductions across the US and the resultant effect on nitrogen loading to the Gulf of Mexico from the Mississippi River Basin. This is a demonstration of a capability to connect the N cascade bringing air, land, water together. EPIC = Environmental Policy Integrated Climate model, CMAQ = Community Multiscale Air Quality model, NEWS = Nutrient Export of WaterSheds model. The National Exposure Research Laboratory (NERL) Atmospheric Modeling and Analysis Division (AMAD) conducts research in support of EPA mission to protect human health and the environment. AMAD research program is engaged in developing and evaluating predictive atmospheric models on all spatial and temporal scales for forecasting the air quality and for assessing changes in air quality and air pollutant exposures, as affected by changes in ecosystem management and regulatory decisions. AMAD is responsible for providing a sound scientific and technical basis for regulatory policies based on air quality models to improve ambient air quality. The models developed by AMAD are being used by EPA, NOAA, and the air pollution community in understanding and forecasting not only the magnitude of the air pollution problem, but also in developing emission control policies and regulations for air quality improvements.

Vegetation Exposure to Ozone over the Continental United States: Assessment of Exposure Indices by the Eta-CMAQ Air Quality Forecast Model

EPA Science Inventory

This study presents the first evaluation of the performance of the Eta-CMAQ air quality forecast model to predict a variety of widely used seasonal mean and cumulative O₃ exposure indices associated with vegetation using the U.S. AIRNow O₃ observations.

Integrating Multiple Criteria in Selection Procedures for Improving Student Quality and Reducing Cost Per Graduate. AIR Forum 1979 Paper.

ERIC Educational Resources Information Center

Jones, Gerald L.; Westen, Risdon J.

The multivariate approach of canonical correlation was used to assess selection procedures of the Air Force Academy. It was felt that improved student selection methods might reduce the number of dropouts while maintaining or improving the quality of graduates. The method of canonical correlation was designed to maximize prediction of academic…

Urban Air Quality Modelling with AURORA: Prague and Bratislava

NASA Astrophysics Data System (ADS)

Veldeman, N.; Viaene, P.; De Ridder, K.; Peelaerts, W.; Lauwaet, D.; Muhammad, N.; Blyth, L.

2012-04-01

The European Commission, in its strategy to protect the health of the European citizens, states that in order to assess the impact of air pollution on public health, information on long-term exposure to air pollution should be available. Currently, indicators of air quality are often being generated using measured pollutant concentrations. While air quality monitoring stations data provide accurate time series information at specific locations, air quality models have the advantage of being able to assess the spatial variability of air quality (for different resolutions) and predict air quality in the future based on different scenarios. When running such air quality models at a high spatial and temporal resolution, one can simulate the actual situation as closely as possible, allowing for a detailed assessment of the risk of exposure to citizens from different pollutants. AURORA (Air quality modelling in Urban Regions using an Optimal Resolution Approach), a prognostic 3-dimensional Eulerian chemistry-transport model, is designed to simulate urban- to regional-scale atmospheric pollutant concentration and exposure fields. The AURORA model also allows to calculate the impact of changes in land use (e.g. planting of trees) or of emission reduction scenario's on air quality. AURORA is currently being applied within the ESA atmospheric GMES service, PASODOBLE (http://www.myair-eu.org), that delivers information on air quality, greenhouse gases, stratospheric ozone, … At present there are two operational AURORA services within PASODOBLE. Within the "Air quality forecast service" VITO delivers daily air quality forecasts for Belgium at a resolution of 5 km and for the major Belgian cities: Brussels, Ghent, Antwerp, Liege and Charleroi. Furthermore forecast services are provided for Prague, Czech Republic and Bratislava, Slovakia, both at a resolution of 1 km. The "Urban/regional air quality assessment service" provides urban- and regional-scale maps (hourly resolution) for air pollution and human exposure statistics for an entire year. So far we concentrated on Brussels, Belgium and the Rotterdam harbour area, The Netherlands. In this contribution we focus on the operational forecast services. Reference Lefebvre W. et al. (2011) Validation of the MIMOSA-AURORA-IFDM model chain for policy support: Modeling concentrations of elemental carbon in Flanders, Atmospheric Environment 45, 6705-6713

The Atlanta Urban Heat Island Mitigation and Air Quality Modeling Project: How High-Resoution Remote Sensing Data Can Improve Air Quality Models

NASA Technical Reports Server (NTRS)

Quattrochi, Dale A.; Estes, Maurice G., Jr.; Crosson, William L.; Khan, Maudood N.

2006-01-01

The Atlanta Urban Heat Island and Air Quality Project had its genesis in Project ATLANTA (ATlanta Land use Analysis: Temperature and Air quality) that began in 1996. Project ATLANTA examined how high-spatial resolution thermal remote sensing data could be used to derive better measurements of the Urban Heat Island effect over Atlanta. We have explored how these thermal remote sensing, as well as other imaged datasets, can be used to better characterize the urban landscape for improved air quality modeling over the Atlanta area. For the air quality modeling project, the National Land Cover Dataset and the local scale Landpro99 dataset at 30m spatial resolutions have been used to derive land use/land cover characteristics for input into the MM5 mesoscale meteorological model that is one of the foundations for the Community Multiscale Air Quality (CMAQ) model to assess how these data can improve output from CMAQ. Additionally, land use changes to 2030 have been predicted using a Spatial Growth Model (SGM). SGM simulates growth around a region using population, employment and travel demand forecasts. Air quality modeling simulations were conducted using both current and future land cover. Meteorological modeling simulations indicate a 0.5 C increase in daily maximum air temperatures by 2030. Air quality modeling simulations show substantial differences in relative contributions of individual atmospheric pollutant constituents as a result of land cover change. Enhanced boundary layer mixing over the city tends to offset the increase in ozone concentration expected due to higher surface temperatures as a result of urbanization.

The influence of tree stands and a noise barrier on near-roadway air quality

EPA Science Inventory

Prediction of air pollution exposure levels of people living near or commuting on roadways is still very problematic due to the highly localized nature of traffic intensity, fleet composition, and extremely complex air flow patterns in urban areas. Both modelling and field studie...

"Going the Extra Mile in Downscaling: Why Downscaling is not ...

EPA Pesticide Factsheets

This presentation provides an example of doing additional work for preprocessing global climate model data for use in regional climate modeling simulations with the Weather Research and Forecasting (WRF) model. In this presentation, results from 15 months of downscaling the Community Earth System Model (CESM) were shown, both using the out-of-the-box downscaling of CESM and also with a modification to setting the inland lake temperatures. The National Exposure Research Laboratory (NERL) Atmospheric Modeling and Analysis Division (AMAD) conducts research in support of EPA mission to protect human health and the environment. AMAD research program is engaged in developing and evaluating predictive atmospheric models on all spatial and temporal scales for forecasting the air quality and for assessing changes in air quality and air pollutant exposures, as affected by changes in ecosystem management and regulatory decisions. AMAD is responsible for providing a sound scientific and technical basis for regulatory policies based on air quality models to improve ambient air quality. The models developed by AMAD are being used by EPA, NOAA, and the air pollution community in understanding and forecasting not only the magnitude of the air pollution problem, but also in developing emission control policies and regulations for air quality improvements.

"Updates to Model Algorithms & Inputs for the Biogenic ...

EPA Pesticide Factsheets

We have developed new canopy emission algorithms and land use data for BEIS. Simulations with BEIS v3.4 and these updates in CMAQ v5.0.2 are compared these changes to the Model of Emissions of Gases and Aerosols from Nature (MEGAN) and evaluated the simulations against observations. This has resulted in improvements in model evaluations of modeled isoprene, NOx, and O3. The National Exposure Research Laboratory (NERL) Atmospheric Modeling and Analysis Division (AMAD) conducts research in support of EPA mission to protect human health and the environment. AMAD research program is engaged in developing and evaluating predictive atmospheric models on all spatial and temporal scales for forecasting the air quality and for assessing changes in air quality and air pollutant exposures, as affected by changes in ecosystem management and regulatory decisions. AMAD is responsible for providing a sound scientific and technical basis for regulatory policies based on air quality models to improve ambient air quality. The models developed by AMAD are being used by EPA, NOAA, and the air pollution community in understanding and forecasting not only the magnitude of the air pollution problem, but also in developing emission control policies and regulations for air quality improvements.

"Total Deposition (TDEP) Maps" | Science Inventory | US EPA

EPA Pesticide Factsheets

The presentation provides an update on the use of a hybrid methodology that relies on measured values from national monitoring networks and modeled values from CMAQ to produce of maps of total deposition for use in critical loads and other ecological assessments. Additionally, comparisons of the deposition values from the hybrid approach are compared with deposition estimates from other methodologies. The National Exposure Research Laboratory (NERL) Atmospheric Modeling and Analysis Division (AMAD) conducts research in support of EPA mission to protect human health and the environment. AMAD research program is engaged in developing and evaluating predictive atmospheric models on all spatial and temporal scales for forecasting the air quality and for assessing changes in air quality and air pollutant exposures, as affected by changes in ecosystem management and regulatory decisions. AMAD is responsible for providing a sound scientific and technical basis for regulatory policies based on air quality models to improve ambient air quality. The models developed by AMAD are being used by EPA, NOAA, and the air pollution community in understanding and forecasting not only the magnitude of the air pollution problem, but also in developing emission control policies and regulations for air quality improvements.

Canadian Operational Air Quality Forecasting Systems: Status, Recent Progress, and Challenges

NASA Astrophysics Data System (ADS)

Pavlovic, Radenko; Davignon, Didier; Ménard, Sylvain; Munoz-Alpizar, Rodrigo; Landry, Hugo; Beaulieu, Paul-André; Gilbert, Samuel; Moran, Michael; Chen, Jack

2017-04-01

ECCC's Canadian Meteorological Centre Operations (CMCO) division runs a number of operational air quality (AQ)-related systems that revolve around the Regional Air Quality Deterministic Prediction System (RAQDPS). The RAQDPS generates 48-hour AQ forecasts and outputs hourly concentration fields of O3, PM2.5, NO2, and other pollutants twice daily on a North-American domain with 10-km horizontal grid spacing and 80 vertical levels. A closely related AQ forecast system with near-real-time wildfire emissions, known as FireWork, has been run by CMCO during the Canadian wildfire season (April to October) since 2014. This system became operational in June 2016. The CMCO`s operational AQ forecast systems also benefit from several support systems, such as a statistical post-processing model called UMOS-AQ that is applied to enhance forecast reliability at point locations with AQ monitors. The Regional Deterministic Air Quality Analysis (RDAQA) system has also been connected to the RAQDPS since February 2013, and hourly surface objective analyses are now available for O3, PM2.5, NO2, PM10, SO2 and, indirectly, the Canadian Air Quality Health Index. As of June 2015, another version of the RDAQA has been connected to FireWork (RDAQA-FW). For verification purposes, CMCO developed a third support system called Verification for Air QUality Models (VAQUM), which has a geospatial relational database core and which enables continuous monitoring of the AQ forecast systems' performance. Urban environments are particularly subject to AQ pollution. In order to improve the services offered, ECCC has recently been investing efforts to develop a high resolution air quality prediction capability for urban areas in Canada. In this presentation, a comprehensive description of the ECCC AQ systems will be provided, along with a discussion on AQ systems performance. Recent improvements, current challenges, and future directions of the Canadian operational AQ program will also be discussed.

Integrated analysis of numerical weather prediction and computational fluid dynamics for estimating cross-ventilation effects on inhaled air quality inside a factory

NASA Astrophysics Data System (ADS)

Murga, Alicia; Sano, Yusuke; Kawamoto, Yoichi; Ito, Kazuhide

2017-10-01

Mechanical and passive ventilation strategies directly impact indoor air quality. Passive ventilation has recently become widespread owing to its ability to reduce energy demand in buildings, such as the case of natural or cross ventilation. To understand the effect of natural ventilation on indoor environmental quality, outdoor-indoor flow paths need to be analyzed as functions of urban atmospheric conditions, topology of the built environment, and indoor conditions. Wind-driven natural ventilation (e.g., cross ventilation) can be calculated through the wind pressure coefficient distributions of outdoor wall surfaces and openings of a building, allowing the study of indoor air parameters and airborne contaminant concentrations. Variations in outside parameters will directly impact indoor air quality and residents' health. Numerical modeling can contribute to comprehend these various parameters because it allows full control of boundary conditions and sampling points. In this study, numerical weather prediction modeling was used to calculate wind profiles/distributions at the atmospheric scale, and computational fluid dynamics was used to model detailed urban and indoor flows, which were then integrated into a dynamic downscaling analysis to predict specific urban wind parameters from the atmospheric to built-environment scale. Wind velocity and contaminant concentration distributions inside a factory building were analyzed to assess the quality of the human working environment by using a computer simulated person. The impact of cross ventilation flows and its variations on local average contaminant concentration around a factory worker, and inhaled contaminant dose, were then discussed.

Characterizing air quality data from complex network perspective.

PubMed

Fan, Xinghua; Wang, Li; Xu, Huihui; Li, Shasha; Tian, Lixin

2016-02-01

Air quality depends mainly on changes in emission of pollutants and their precursors. Understanding its characteristics is the key to predicting and controlling air quality. In this study, complex networks were built to analyze topological characteristics of air quality data by correlation coefficient method. Firstly, PM2.5 (particulate matter with aerodynamic diameter less than 2.5 μm) indexes of eight monitoring sites in Beijing were selected as samples from January 2013 to December 2014. Secondly, the C-C method was applied to determine the structure of phase space. Points in the reconstructed phase space were considered to be nodes of the network mapped. Then, edges were determined by nodes having the correlation greater than a critical threshold. Three properties of the constructed networks, degree distribution, clustering coefficient, and modularity, were used to determine the optimal value of the critical threshold. Finally, by analyzing and comparing topological properties, we pointed out that similarities and difference in the constructed complex networks revealed influence factors and their different roles on real air quality system.

Cost analysis of impacts of climate change on regional air quality.

PubMed

Liao, Kuo-Jen; Tagaris, Efthimios; Russell, Armistead G; Amar, Praveen; He, Shan; Manomaiphiboon, Kasemsan; Woo, Jung-Hun

2010-02-01

Climate change has been predicted to adversely impact regional air quality with resulting health effects. Here a regional air quality model and a technology analysis tool are used to assess the additional emission reductions required and associated costs to offset impacts of climate change on air quality. Analysis is done for six regions and five major cities in the continental United States. Future climate is taken from a global climate model simulation for 2049-2051 using the Intergovernmental Panel on Climate Change (IPCC) A1B emission scenario, and emission inventories are the same as current ones to assess impacts of climate change alone on air quality and control expenses. On the basis of the IPCC A1B emission scenario and current control technologies, least-cost sets of emission reductions for simultaneously offsetting impacts of climate change on regionally averaged 4th highest daily maximum 8-hr average ozone and yearly averaged PM2.5 (particulate matter [PM] with an aerodynamic diameter less than 2.5 microm) for the six regions examined are predicted to range from $36 million (1999$) yr(-1) in the Southeast to $5.5 billion yr(-1) in the Northeast. However, control costs to offset climate-related pollutant increases in urban areas can be greater than the regional costs because of the locally exacerbated ozone levels. An annual cost of $4.1 billion is required for offsetting climate-induced air quality impairment in 2049-2051 in the five cities alone. Overall, an annual cost of $9.3 billion is estimated for offsetting climate change impacts on air quality for the six regions and five cities examined. Much of the additional expense is to reduce increased levels of ozone. Additional control costs for offsetting the impacts everywhere in the United States could be larger than the estimates in this study. This study shows that additional emission controls and associated costs for offsetting climate impacts could significantly increase currently estimated control requirements and should be considered in developing control strategies for achieving air quality targets in the future.

Ensemble prediction of air quality using the WRF/CMAQ model system for health effect studies in China

NASA Astrophysics Data System (ADS)

Hu, Jianlin; Li, Xun; Huang, Lin; Ying, Qi; Zhang, Qiang; Zhao, Bin; Wang, Shuxiao; Zhang, Hongliang

2017-11-01

Accurate exposure estimates are required for health effect analyses of severe air pollution in China. Chemical transport models (CTMs) are widely used to provide spatial distribution, chemical composition, particle size fractions, and source origins of air pollutants. The accuracy of air quality predictions in China is greatly affected by the uncertainties of emission inventories. The Community Multiscale Air Quality (CMAQ) model with meteorological inputs from the Weather Research and Forecasting (WRF) model were used in this study to simulate air pollutants in China in 2013. Four simulations were conducted with four different anthropogenic emission inventories, including the Multi-resolution Emission Inventory for China (MEIC), the Emission Inventory for China by School of Environment at Tsinghua University (SOE), the Emissions Database for Global Atmospheric Research (EDGAR), and the Regional Emission inventory in Asia version 2 (REAS2). Model performance of each simulation was evaluated against available observation data from 422 sites in 60 cities across China. Model predictions of O3 and PM2.5 generally meet the model performance criteria, but performance differences exist in different regions, for different pollutants, and among inventories. Ensemble predictions were calculated by linearly combining the results from different inventories to minimize the sum of the squared errors between the ensemble results and the observations in all cities. The ensemble concentrations show improved agreement with observations in most cities. The mean fractional bias (MFB) and mean fractional errors (MFEs) of the ensemble annual PM2.5 in the 60 cities are -0.11 and 0.24, respectively, which are better than the MFB (-0.25 to -0.16) and MFE (0.26-0.31) of individual simulations. The ensemble annual daily maximum 1 h O3 (O3-1h) concentrations are also improved, with mean normalized bias (MNB) of 0.03 and mean normalized errors (MNE) of 0.14, compared to MNB of 0.06-0.19 and MNE of 0.16-0.22 of the individual predictions. The ensemble predictions agree better with observations with daily, monthly, and annual averaging times in all regions of China for both PM2.5 and O3-1h. The study demonstrates that ensemble predictions from combining predictions from individual emission inventories can improve the accuracy of predicted temporal and spatial distributions of air pollutants. This study is the first ensemble model study in China using multiple emission inventories, and the results are publicly available for future health effect studies.

A Flexible Spatio-Temporal Model for Air Pollution with Spatial and Spatio-Temporal Covariates.

PubMed

Lindström, Johan; Szpiro, Adam A; Sampson, Paul D; Oron, Assaf P; Richards, Mark; Larson, Tim V; Sheppard, Lianne

2014-09-01

The development of models that provide accurate spatio-temporal predictions of ambient air pollution at small spatial scales is of great importance for the assessment of potential health effects of air pollution. Here we present a spatio-temporal framework that predicts ambient air pollution by combining data from several different monitoring networks and deterministic air pollution model(s) with geographic information system (GIS) covariates. The model presented in this paper has been implemented in an R package, SpatioTemporal, available on CRAN. The model is used by the EPA funded Multi-Ethnic Study of Atherosclerosis and Air Pollution (MESA Air) to produce estimates of ambient air pollution; MESA Air uses the estimates to investigate the relationship between chronic exposure to air pollution and cardiovascular disease. In this paper we use the model to predict long-term average concentrations of NO x in the Los Angeles area during a ten year period. Predictions are based on measurements from the EPA Air Quality System, MESA Air specific monitoring, and output from a source dispersion model for traffic related air pollution (Caline3QHCR). Accuracy in predicting long-term average concentrations is evaluated using an elaborate cross-validation setup that accounts for a sparse spatio-temporal sampling pattern in the data, and adjusts for temporal effects. The predictive ability of the model is good with cross-validated R 2 of approximately 0.7 at subject sites. Replacing four geographic covariate indicators of traffic density with the Caline3QHCR dispersion model output resulted in very similar prediction accuracy from a more parsimonious and more interpretable model. Adding traffic-related geographic covariates to the model that included Caline3QHCR did not further improve the prediction accuracy.

Unusually high soil nitrogen oxide emissions influence air quality in a high-temperature agricultural region

PubMed Central

Oikawa, P. Y.; Ge, C.; Wang, J.; Eberwein, J. R.; Liang, L. L.; Allsman, L. A.; Grantz, D. A.; Jenerette, G. D.

2015-01-01

Fertilized soils have large potential for production of soil nitrogen oxide (NOx=NO+NO2), however these emissions are difficult to predict in high-temperature environments. Understanding these emissions may improve air quality modelling as NOx contributes to formation of tropospheric ozone (O3), a powerful air pollutant. Here we identify the environmental and management factors that regulate soil NOx emissions in a high-temperature agricultural region of California. We also investigate whether soil NOx emissions are capable of influencing regional air quality. We report some of the highest soil NOx emissions ever observed. Emissions vary nonlinearly with fertilization, temperature and soil moisture. We find that a regional air chemistry model often underestimates soil NOx emissions and NOx at the surface and in the troposphere. Adjusting the model to match NOx observations leads to elevated tropospheric O3. Our results suggest management can greatly reduce soil NOx emissions, thereby improving air quality. PMID:26556236

Improving of local ozone forecasting by integrated models.

PubMed

Gradišar, Dejan; Grašič, Boštjan; Božnar, Marija Zlata; Mlakar, Primož; Kocijan, Juš

2016-09-01

This paper discuss the problem of forecasting the maximum ozone concentrations in urban microlocations, where reliable alerting of the local population when thresholds have been surpassed is necessary. To improve the forecast, the methodology of integrated models is proposed. The model is based on multilayer perceptron neural networks that use as inputs all available information from QualeAria air-quality model, WRF numerical weather prediction model and onsite measurements of meteorology and air pollution. While air-quality and meteorological models cover large geographical 3-dimensional space, their local resolution is often not satisfactory. On the other hand, empirical methods have the advantage of good local forecasts. In this paper, integrated models are used for improved 1-day-ahead forecasting of the maximum hourly value of ozone within each day for representative locations in Slovenia. The WRF meteorological model is used for forecasting meteorological variables and the QualeAria air-quality model for gas concentrations. Their predictions, together with measurements from ground stations, are used as inputs to a neural network. The model validation results show that integrated models noticeably improve ozone forecasts and provide better alert systems.

Towards an agent based traffic regulation and recommendation system for the on-road air quality control.

PubMed

Sadiq, Abderrahmane; El Fazziki, Abdelaziz; Ouarzazi, Jamal; Sadgal, Mohamed

2016-01-01

This paper presents an integrated and adaptive problem-solving approach to control the on-road air quality by modeling the road infrastructure, managing traffic based on pollution level and generating recommendations for road users. The aim is to reduce vehicle emissions in the most polluted road segments and optimizing the pollution levels. For this we propose the use of historical and real time pollution records and contextual data to calculate the air quality index on road networks and generate recommendations for reassigning traffic flow in order to improve the on-road air quality. The resulting air quality indexes are used in the system's traffic network generation, which the cartography is represented by a weighted graph. The weights evolve according to the pollution indexes and path properties and the graph is therefore dynamic. Furthermore, the systems use the available pollution data and meteorological records in order to predict the on-road pollutant levels by using an artificial neural network based prediction model. The proposed approach combines the benefits of multi-agent systems, Big data technology, machine learning tools and the available data sources. For the shortest path searching in the road network, we use the Dijkstra algorithm over Hadoop MapReduce framework. The use Hadoop framework in the data retrieve and analysis process has significantly improved the performance of the proposed system. Also, the agent technology allowed proposing a suitable solution in terms of robustness and agility.

Multiscale predictions of aviation-attributable PM2.5 for U.S. airports modeled using CMAQ with plume-in-grid and an aircraft-specific 1-D emission model

EPA Science Inventory

Aviation activities represent an important and unique mode of transportation, but also impact air quality. In this study, we aim to quantify the impact of aircraft on air quality, focusing on aviation-attributable PM2.5 at scales ranging from local (a few kilometers) to continent...

Improvement of Meteorological Inputs for TexAQS-II Air Quality Simulations

NASA Astrophysics Data System (ADS)

Ngan, F.; Byun, D.; Kim, H.; Cheng, F.; Kim, S.; Lee, D.

2008-12-01

An air quality forecasting system (UH-AQF) for Eastern Texas, which is in operation by the Institute for Multidimensional Air Quality Studies (IMAQS) at the University of Houston, uses the Fifth-Generation PSU/NCAR Mesoscale Model MM5 model as the meteorological driver for modeling air quality with the Community Multiscale Air Quality (CMAQ) model. While the forecasting system was successfully used for the planning and implementation of various measurement activities, evaluations of the forecasting results revealed a few systematic problems in the numerical simulations. From comparison with observations, we observe some times over-prediction of northerly winds caused by inaccurate synoptic inputs and other times too strong southerly winds caused by local sea breeze development. Discrepancies in maximum and minimum temperature are also seen for certain days. Precipitation events, as well as clouds, are simulated at the incorrect locations and times occasionally. Model simulatednrealistic thunderstorms are simulated, causing sometimes cause unrealistically strong outflows. To understand physical and chemical processes influencing air quality measures, a proper description of real world meteorological conditions is essential. The objective of this study is to generate better meteorological inputs than the AQF results to support the chemistry modeling. We utilized existing objective analysis and nudging tools in the MM5 system to develop the MUltiscale Nest-down Data Assimilation System (MUNDAS), which incorporates extensive meteorological observations available in the simulated domain for the retrospective simulation of the TexAQS-II period. With the re-simulated meteorological input, we are able to better predict ozone events during TexAQS-II period. In addition, base datasets in MM5 such as land use/land cover, vegetation fraction, soil type and sea surface temperature are updated by satellite data to represent the surface features more accurately. They are key physical parameters inputs affecting transfer of heat, momentum and soil moisture in land-surface process in MM5. Using base the accurate input datasets, we are able to have improved see the differences of predictions of ground temperatures, winds and even thunderstorm activities within boundary layer.

Developing a methodology to predict PM10 concentrations in urban areas using generalized linear models.

PubMed

Garcia, J M; Teodoro, F; Cerdeira, R; Coelho, L M R; Kumar, Prashant; Carvalho, M G

2016-09-01

A methodology to predict PM10 concentrations in urban outdoor environments is developed based on the generalized linear models (GLMs). The methodology is based on the relationship developed between atmospheric concentrations of air pollutants (i.e. CO, NO2, NOx, VOCs, SO2) and meteorological variables (i.e. ambient temperature, relative humidity (RH) and wind speed) for a city (Barreiro) of Portugal. The model uses air pollution and meteorological data from the Portuguese monitoring air quality station networks. The developed GLM considers PM10 concentrations as a dependent variable, and both the gaseous pollutants and meteorological variables as explanatory independent variables. A logarithmic link function was considered with a Poisson probability distribution. Particular attention was given to cases with air temperatures both below and above 25°C. The best performance for modelled results against the measured data was achieved for the model with values of air temperature above 25°C compared with the model considering all ranges of air temperatures and with the model considering only temperature below 25°C. The model was also tested with similar data from another Portuguese city, Oporto, and results found to behave similarly. It is concluded that this model and the methodology could be adopted for other cities to predict PM10 concentrations when these data are not available by measurements from air quality monitoring stations or other acquisition means.

Do causal concentration-response functions exist? A critical review of associational and causal relations between fine particulate matter and mortality.

PubMed

Cox, Louis Anthony Tony

2017-08-01

Concentration-response (C-R) functions relating concentrations of pollutants in ambient air to mortality risks or other adverse health effects provide the basis for many public health risk assessments, benefits estimates for clean air regulations, and recommendations for revisions to existing air quality standards. The assumption that C-R functions relating levels of exposure and levels of response estimated from historical data usefully predict how future changes in concentrations would change risks has seldom been carefully tested. This paper critically reviews literature on C-R functions for fine particulate matter (PM2.5) and mortality risks. We find that most of them describe historical associations rather than valid causal models for predicting effects of interventions that change concentrations. The few papers that explicitly attempt to model causality rely on unverified modeling assumptions, casting doubt on their predictions about effects of interventions. A large literature on modern causal inference algorithms for observational data has been little used in C-R modeling. Applying these methods to publicly available data from Boston and the South Coast Air Quality Management District around Los Angeles shows that C-R functions estimated for one do not hold for the other. Changes in month-specific PM2.5 concentrations from one year to the next do not help to predict corresponding changes in average elderly mortality rates in either location. Thus, the assumption that estimated C-R relations predict effects of pollution-reducing interventions may not be true. Better causal modeling methods are needed to better predict how reducing air pollution would affect public health.

A QSPR model for prediction of diffusion coefficient of non-electrolyte organic compounds in air at ambient condition.

PubMed

Mirkhani, Seyyed Alireza; Gharagheizi, Farhad; Sattari, Mehdi

2012-03-01

Evaluation of diffusion coefficients of pure compounds in air is of great interest for many diverse industrial and air quality control applications. In this communication, a QSPR method is applied to predict the molecular diffusivity of chemical compounds in air at 298.15K and atmospheric pressure. Four thousand five hundred and seventy nine organic compounds from broad spectrum of chemical families have been investigated to propose a comprehensive and predictive model. The final model is derived by Genetic Function Approximation (GFA) and contains five descriptors. Using this dedicated model, we obtain satisfactory results quantified by the following statistical results: Squared Correlation Coefficient=0.9723, Standard Deviation Error=0.003 and Average Absolute Relative Deviation=0.3% for the predicted properties from existing experimental values. Copyright © 2011 Elsevier Ltd. All rights reserved.

Prediction of Air Pollutants Concentration Based on an Extreme Learning Machine: The Case of Hong Kong

PubMed Central

Zhang, Jiangshe; Ding, Weifu

2017-01-01

With the development of the economy and society all over the world, most metropolitan cities are experiencing elevated concentrations of ground-level air pollutants. It is urgent to predict and evaluate the concentration of air pollutants for some local environmental or health agencies. Feed-forward artificial neural networks have been widely used in the prediction of air pollutants concentration. However, there are some drawbacks, such as the low convergence rate and the local minimum. The extreme learning machine for single hidden layer feed-forward neural networks tends to provide good generalization performance at an extremely fast learning speed. The major sources of air pollutants in Hong Kong are mobile, stationary, and from trans-boundary sources. We propose predicting the concentration of air pollutants by the use of trained extreme learning machines based on the data obtained from eight air quality parameters in two monitoring stations, including Sham Shui Po and Tap Mun in Hong Kong for six years. The experimental results show that our proposed algorithm performs better on the Hong Kong data both quantitatively and qualitatively. Particularly, our algorithm shows better predictive ability, with R2 increased and root mean square error values decreased respectively. PMID:28125034

Current Applications of OMI Tropospheric NO2 Data for Air Quality and a Look to the Future

NASA Technical Reports Server (NTRS)

Pickering, Kenneth E.; Bucsela, E.; Allen, D.; Prados, A.; Gleason, J.; Kondragunta, S.

2010-01-01

Ozone Monitoring Instrument (OMI) Tropospheric NO2 products are being used to enhance the ability to monitor changes in NO2 air quality, update emission inventories, and evaluate regional air quality models. Trends in tropospheric column NO2 have been examined over the eastern United States in relation to emissions changes mandated by regulatory actions. Decreases of 20 to 40 percent over the period 2005 to 2008 were noted, largely in response to major emission reductions at power plants. The OMI data have been used to identify regions in which the opposite trend has been found. We have also used OMI NO2 in efforts to improve emission inventories for NOx emissions from soil. Lightning NOx emissions have been added to CMAQ, the US Environmental Protection Agency's regional air quality model. Evaluation of the resulting NO2 columns in the model is being conducted using the OMI NO2 observations. Community Multiscale Air Quality (CMAQ) together with the OMI NO2 data comprise a valuable tool for monitoring and predicting air quality. Looking to the future, we expect that the combination of Global Ozone Monitoring Experiment-2 (GOME-2) (morning) and OMI (afternoon) data sets obtained through use of the same retrieval algorithms will substantially increase the possibility of successful integration of satellite information into regional air quality forecast models. Farther down the road, we anticipate the Geostationary Coastal and Air Pollution Events (GEO-CAPE) platform to supply data possibly on an hourly basis, allowing much more comprehensive analysis of air quality from space.

Application of Wavelet Filters in an Evaluation of ...

EPA Pesticide Factsheets

Air quality model evaluation can be enhanced with time-scale specific comparisons of outputs and observations. For example, high-frequency (hours to one day) time scale information in observed ozone is not well captured by deterministic models and its incorporation into model performance metrics lead one to devote resources to stochastic variations in model outputs. In this analysis, observations are compared with model outputs at seasonal, weekly, diurnal and intra-day time scales. Filters provide frequency specific information that can be used to compare the strength (amplitude) and timing (phase) of observations and model estimates. The National Exposure Research Laboratory′s (NERL′s) Atmospheric Modeling and Analysis Division (AMAD) conducts research in support of EPA′s mission to protect human health and the environment. AMAD′s research program is engaged in developing and evaluating predictive atmospheric models on all spatial and temporal scales for forecasting the Nation′s air quality and for assessing changes in air quality and air pollutant exposures, as affected by changes in ecosystem management and regulatory decisions. AMAD is responsible for providing a sound scientific and technical basis for regulatory policies based on air quality models to improve ambient air quality. The models developed by AMAD are being used by EPA, NOAA, and the air pollution community in understanding and forecasting not only the magnitude of the air pollu

A Spectral Method for Spatial Downscaling

PubMed Central

Reich, Brian J.; Chang, Howard H.; Foley, Kristen M.

2014-01-01

Summary Complex computer models play a crucial role in air quality research. These models are used to evaluate potential regulatory impacts of emission control strategies and to estimate air quality in areas without monitoring data. For both of these purposes, it is important to calibrate model output with monitoring data to adjust for model biases and improve spatial prediction. In this article, we propose a new spectral method to study and exploit complex relationships between model output and monitoring data. Spectral methods allow us to estimate the relationship between model output and monitoring data separately at different spatial scales, and to use model output for prediction only at the appropriate scales. The proposed method is computationally efficient and can be implemented using standard software. We apply the method to compare Community Multiscale Air Quality (CMAQ) model output with ozone measurements in the United States in July 2005. We find that CMAQ captures large-scale spatial trends, but has low correlation with the monitoring data at small spatial scales. PMID:24965037

A Multi-Model Assessment for the 2006 and 2010 Simulations under the Air Quality Model Evaluation International Initiative (AQMEII) Phase 2 over North America: Part II. Evaluation of Column Variable Predictions Using Satellite Data

EPA Science Inventory

Within the context of the Air Quality Model Evaluation International Initiative phase 2 (AQMEII2) project, this part II paper performs a multi-model assessment of major column abundances of gases, radiation, aerosol, and cloud variables for 2006 and 2010 simulations with three on...

What Air Quality Models Tell Us About Sources and Sinks of Atmospheric Aldehydes

NASA Astrophysics Data System (ADS)

Luecken, D.; Hutzell, W. T.; Phillips, S.

2010-12-01

Atmospheric aldehydes play important roles in several aspects of air quality: they are critical radical sources that drive ozone formation, they are hazardous air pollutants that are national drivers for cancer risk, they participate in aqueous chemistry and potentially aerosol formation, and are key species for evaluating the accuracy of isoprene emissions. For these reasons, it is important to accurately understand their sources and sinks, and the sensitivity of their concentrations to emission controls. While both compounds have been included in air quality modeling for many years, current, state-of-the-science chemical mechanisms have difficulty reproducing measured values of aldehydes, which calls into question the robustness of ozone, HAPs and aerosol predictions. In the past, we have attributed discrepancies to measurement errors, inventory errors, or the focus on high-NOx urban regimes. Despite improvements in all of these areas, the measurements still diverge from model predictions, with formaldehyde often underpredicted by 50% and acetaldehyde showing a large degree of scatter - from 20% overprediction to 50% underprediction. To better examine the sources of aldehydes, we implemented the new SAPRC07T mechanism in the Community Multi-Scale Air Quality (CMAQ) model. This mechanism incorporates current recommendations for kinetic data and has the most detailed representation of product formation under a wide variety of conditions of any mechanism used in regional air quality models. We use model simulations to pinpoint where and when aldehyde concentrations tend to deviate from measurements. We demonstrate the role of secondary production versus primary emissions in aldehdye concentrations and find that secondary sources produce the largest deviations from measurements. We identify which VOCs are most responsible for aldehyde secondary production in the areas of the U.S. where the largest health effects are seen, and discuss how this affects consideration of control strategies.

Determining the Uncertainties in Prescribed Burn Emissions Through Comparison of Satellite Estimates to Ground-based Estimates and Air Quality Model Evaluations in Southeastern US

NASA Astrophysics Data System (ADS)

Odman, M. T.; Hu, Y.; Russell, A. G.

2016-12-01

Prescribed burning is practiced throughout the US, and most widely in the Southeast, for the purpose of maintaining and improving the ecosystem, and reducing the wildfire risk. However, prescribed burn emissions contribute significantly to the of trace gas and particulate matter loads in the atmosphere. In places where air quality is already stressed by other anthropogenic emissions, prescribed burns can lead to major health and environmental problems. Air quality modeling efforts are under way to assess the impacts of prescribed burn emissions. Operational forecasts of the impacts are also emerging for use in dynamic management of air quality as well as the burns. Unfortunately, large uncertainties exist in the process of estimating prescribed burn emissions and these uncertainties limit the accuracy of the burn impact predictions. Prescribed burn emissions are estimated by using either ground-based information or satellite observations. When there is sufficient local information about the burn area, the types of fuels, their consumption amounts, and the progression of the fire, ground-based estimates are more accurate. In the absence of such information satellites remain as the only reliable source for emission estimation. To determine the level of uncertainty in prescribed burn emissions, we compared estimates derived from a burn permit database and other ground-based information to the estimates by the Biomass Burning Emissions Product derived from a constellation of NOAA and NASA satellites. Using these emissions estimates we conducted simulations with the Community Multiscale Air Quality (CMAQ) model and predicted trace gas and particulate matter concentrations throughout the Southeast for two consecutive burn seasons (2015 and 2016). In this presentation, we will compare model predicted concentrations to measurements at monitoring stations and evaluate if the differences are commensurate with our emission uncertainty estimates. We will also investigate if spatial and temporal patterns in the differences reveal the sources of the uncertainty in the prescribed burn emission estimates.

Predictive model for CO2 generation and decay in building envelopes

NASA Astrophysics Data System (ADS)

Aglan, Heshmat A.

2003-01-01

Understanding carbon dioxide generation and decay patterns in buildings with high occupancy levels is useful to identify their indoor air quality, air change rates, percent fresh air makeup, occupancy pattern, and how a variable air volume system to off-set undesirable CO2 level can be modulated. A mathematical model governing the generation and decay of CO2 in building envelopes with forced ventilation due to high occupancy is developed. The model has been verified experimentally in a newly constructed energy efficient healthy house. It was shown that the model accurately predicts the CO2 concentration at any time during the generation and decay processes.

AIR QUALITY MODELING OF HAZARDOUS POLLUTANTS: CURRENT STATUS AND FUTURE DIRECTIONS

EPA Science Inventory

The paper presents a review of current air toxics modeling applications and discusses possible advanced approaches. Many applications require the ability to predict hot spots from industrial sources or large roadways that are needed for community health and Environmental Justice...

IMPROVE AND APPLY CHEMICAL MECHANISMS FOR DEVELOPING OZONE CONTROL STRATEGIES

EPA Science Inventory

Air quality models that realistically describe the formation of ozone, air toxics, and other pollutants are needed by EPA and state agencies to predict current and future concentrations of these pollutants and develop ways to decrease their concentrations below harmful levels. ...

Analysis of carbon monoxide (CO) with Delhi Finite Line Source (DFLS) in MT Haryono Street, Medan City

NASA Astrophysics Data System (ADS)

Turmuzi, M.; Suryati, I.; Mashaly, E. T.; Batubara, F.

2018-02-01

One source to decrease urban air ambient quality is transportation sector. Important pollutants are released by gas emissions from vehicles are carbon monoxide (CO), hydrocarbons (HC), nitrogen dioxide (NO2), particulate matter and others. The presence of CO pollutants in the ambient air can be predicted by modeling air quality. This study aims to estimate CO concentration resulting from transportation activities using Delhi Finite Line Source (DFLS) model, comparing CO prediction using a DFLS model with CO observation in the field, and determine the suitability of the DFLS model application on the MT Haryono street in Medan City. Research was conducted for 3 days at two sample points with frequency twice daily. Based on research results, the range of CO concentration from observation between 22.903 μg/m3 - 27.484 μg/m3. CO observation is still below the ambient air quality standard. According to the DFLS calculations, the range of CO concentration between 1.499 μg/m3- 2.051 μg/m3. The calculation index of agreement (IOA) validation test obtained value of d = 0.22. The DFLS model is not suitable to be applied on MT Haryono street because many factors affected such as wind direction and wind velocity, ambient air temperature and humidity

[Implementation results of emission standards of air pollutants for thermal power plants: a numerical simulation].

PubMed

Wang, Zhan-Shan; Pan, Li-Bo

2014-03-01

The emission inventory of air pollutants from the thermal power plants in the year of 2010 was set up. Based on the inventory, the air quality of the prediction scenarios by implementation of both 2003-version emission standard and the new emission standard were simulated using Models-3/CMAQ. The concentrations of NO2, SO2, and PM2.5, and the deposition of nitrogen and sulfur in the year of 2015 and 2020 were predicted to investigate the regional air quality improvement by the new emission standard. The results showed that the new emission standard could effectively improve the air quality in China. Compared with the implementation results of the 2003-version emission standard, by 2015 and 2020, the area with NO2 concentration higher than the emission standard would be reduced by 53.9% and 55.2%, the area with SO2 concentration higher than the emission standard would be reduced by 40.0%, the area with nitrogen deposition higher than 1.0 t x km(-2) would be reduced by 75.4% and 77.9%, and the area with sulfur deposition higher than 1.6 t x km(-2) would be reduced by 37.1% and 34.3%, respectively.

Biogenic organic emissions, air quality and climate

NASA Astrophysics Data System (ADS)

Guenther, A. B.

2015-12-01

Living organisms produce copious amounts of a diverse array of metabolites including many volatile organic compounds that are released into the atmosphere. These compounds participate in numerous chemical reactions that influence the atmospheric abundance of important air pollutants and short-lived climate forcers including organic aerosol, ozone and methane. The production and release of these organics are strongly influenced by environmental conditions including air pollution, temperature, solar radiation, and water availability and they are highly sensitive to stress and extreme events. As a result, releases of biogenic organics to the atmosphere have an impact on, and are sensitive to, air quality and climate leading to potential feedback couplings. Their role in linking air quality and climate is conceptually clear but an accurate quantitative representation is needed for predictive models. Progress towards this goal will be presented including numerical model development and assessments of the predictive capability of the Model of Emission of Gases and Aerosols from Nature (MEGAN). Recent studies of processes controlling the magnitude and variations in biogenic organic emissions will be described and observations of their impact on atmospheric composition will be shown. Recent advances and priorities for future research will be discussed including laboratory process studies, long-term measurements, multi-scale regional studies, global satellite observations, and the development of a next generation model for simulating land-atmosphere chemical exchange.

Estimates of ground level TSP, SO sub 2 and HCI for a municipal waste incinerator to be located at Tynes Bay - Bermuda

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kent Simmons, J.A.; Knap, A.H.

1991-04-01

The computer model Industrial Source Complex Short Term (ISCST) was used to study the stack emissions from a refuse incinerator proposed for the inland of Bermuda. The model predicts that the highest ground level pollutant concentrations will occur near Prospect, 800 m to 1,000 m due south of the stack. The authors installed a portable laboratory and instruments at Prospect to begin making air quality baseline measurements. By comparing the model's estimates of the incinerator contribution to the background levels measured at the site they predicted that stack emissions would not cause an increase in TSP or SO{sub 2}. Themore » incinerator will be a significant source of HCI to Bermuda air with ambient levels approaching air quality guidelines.« less

Evaluation of WRF Parameterizations for Air Quality Applications over the Midwest USA

NASA Astrophysics Data System (ADS)

Zheng, Z.; Fu, K.; Balasubramanian, S.; Koloutsou-Vakakis, S.; McFarland, D. M.; Rood, M. J.

2017-12-01

Reliable predictions from Chemical Transport Models (CTMs) for air quality research require accurate gridded weather inputs. In this study, a sensitivity analysis of 17 Weather Research and Forecast (WRF) model runs was conducted to explore the optimum configuration in six physics categories (i.e., cumulus, surface layer, microphysics, land surface model, planetary boundary layer, and longwave/shortwave radiation) for the Midwest USA. WRF runs were initally conducted over four days in May 2011 for a 12 km x 12 km domain over contiguous USA and a nested 4 km x 4 km domain over the Midwest USA (i.e., Illinois and adjacent areas including Iowa, Indiana, and Missouri). Model outputs were evaluated statistically by comparison with meteorological observations (DS337.0, METAR data, and the Water and Atmospheric Resources Monitoring Network) and resulting statistics were compared to benchmark values from the literature. Identified optimum configurations of physics parametrizations were then evaluated for the whole months of May and October 2011 to evaluate WRF model performance for Midwestern spring and fall seasons. This study demonstrated that for the chosen physics options, WRF predicted well temperature (Index of Agreement (IOA) = 0.99), pressure (IOA = 0.99), relative humidity (IOA = 0.93), wind speed (IOA = 0.85), and wind direction (IOA = 0.97). However, WRF did not predict daily precipitation satisfactorily (IOA = 0.16). Developed gridded weather fields will be used as inputs to a CTM ensemble consisting of the Comprehensive Air Quality Model with Extensions to study impacts of chemical fertilizer usage on regional air quality in the Midwest USA.

“AQMEII Status Update” | Science Inventory | US EPA

EPA Pesticide Factsheets

“AQMEII Status Update”This presentation provided an overview and status update of the Air Quality Model Evaluation International Initative (AQMEII) to participants of a workshop of the Task Force on Hemispheric Transport of Air Pollution (TF-HTAP) . In addition, the presentation also outlines the objectives and potential timeline for a possible next phase of AQMEII that would involve a collaboration with the current modeling activities of TF-HTAP. The purpose of the presentation was to provide participants at the HTAP meeting with an overview of current AQMEII activities and timelines and to obtain feedback from HTAP workshop participants regarding HTAP timelines. The National Exposure Research Laboratory (NERL) Atmospheric Modeling and Analysis Division (AMAD) conducts research in support of EPA mission to protect human health and the environment. AMAD research program is engaged in developing and evaluating predictive atmospheric models on all spatial and temporal scales for forecasting the air quality and for assessing changes in air quality and air pollutant exposures, as affected by changes in ecosystem management and regulatory decisions. AMAD is responsible for providing a sound scientific and technical basis for regulatory policies based on air quality models to improve ambient air quality. The models developed by AMAD are being used by EPA, NOAA, and the air pollution community in understanding and forecasting not only the magnitude of the air po

Drying characteristics and quality of bananas under infrared radiation heating

USDA-ARS?s Scientific Manuscript database

Hot air (HA) drying of banana has low drying efficiency and results in undesirable product quality. The objectives of this research were to investigate the feasibility of infrared (IR) heating to improve banana drying rate, evaluate quality of the dried product, and establish models for predicting d...

Research and application of a hybrid model based on dynamic fuzzy synthetic evaluation for establishing air quality forecasting and early warning system: A case study in China.

PubMed

Xu, Yunzhen; Du, Pei; Wang, Jianzhou

2017-04-01

As the atmospheric environment pollution has been becoming more and more serious in China, it is highly desirable to develop a scientific and effective early warning system that plays a great significant role in analyzing and monitoring air quality. However, establishing a robust early warning system for warning the public in advance and ameliorating air quality is not only an extremely challenging task but also a public concerned problem for human health. Most previous studies are focused on improving the prediction accuracy, which usually ignore the significance of uncertainty information and comprehensive evaluation concerning air pollutants. Therefore, in this paper a novel robust early warning system was successfully developed, which consists of three modules: evaluation module, forecasting module and characteristics estimating module. In this system, a new dynamic fuzzy synthetic evaluation is proposed and applied to determine air quality levels and primary pollutants, which can be regarded as the research objectives; Moreover, to further mine and analyze the characteristics of air pollutants, four different distribution functions and interval forecasting method are also employed that can not only provide predictive range, confidence level and the other uncertain information of the pollutants future values, but also assist decision-makers in reducing and controlling the emissions of atmospheric pollutants. Case studies utilizing hourly PM 2.5 , PM 10 and SO 2 data collected from Tianjin and Shanghai in China are applied as illustrative examples to estimate the effectiveness and efficiency of the proposed system. Experimental results obviously indicated that the developed novel early warning system is much suitable for analyzing and monitoring air pollution, which can also add a novel viable option for decision-makers. Copyright © 2017 Elsevier Ltd. All rights reserved.

Air quality in Delhi during the Commonwealth Games

NASA Astrophysics Data System (ADS)

Marrapu, P.; Cheng, Y.; Beig, G.; Sahu, S.; Srinivas, R.; Carmichael, G. R.

2014-10-01

Air quality during the Commonwealth Games (CWG, held in Delhi in October 2010) is analyzed using a new air quality forecasting system established for the games. The CWG stimulated enhanced efforts to monitor and model air quality in the region. The air quality of Delhi during the CWG had high levels of particles with mean values of PM2.5 and PM10 at the venues of 111 and 238 μg m-3, respectively. Black carbon (BC) accounted for ~ 10% of the PM2.5 mass. It is shown that BC, PM2.5 and PM10 concentrations are well predicted, but with positive biases of ~ 25%. The diurnal variations are also well captured, with both the observations and the modeled values showing nighttime maxima and daytime minima. A new emissions inventory, developed as part of this air quality forecasting initiative, is evaluated by comparing the observed and predicted species-species correlations (i.e., BC : CO; BC : PM2.5; PM2.5 : PM10). Assuming that the observations at these sites are representative and that all the model errors are associated with the emissions, then the modeled concentrations and slopes can be made consistent by scaling the emissions by 0.6 for NOx, 2 for CO, and 0.7 for BC, PM2.5, and PM10. The emission estimates for particles are remarkably good considering the uncertainty in the estimates due to the diverse spread of activities and technologies that take place in Delhi and the rapid rates of change. The contribution of various emission sectors including transportation, power, domestic and industry to surface concentrations are also estimated. Transport, domestic and industrial sectors all make significant contributions to PM levels in Delhi, and the sectoral contributions vary spatially within the city. Ozone levels in Delhi are elevated, with hourly values sometimes exceeding 100 ppb. The continued growth of the transport sector is expected to make ozone pollution a more pressing air pollution problem in Delhi. The sector analysis provides useful inputs into the design of strategies to reduce air pollution levels in Delhi. The contribution for sources outside of Delhi on Delhi air quality range from ~ 25% for BC and PM to ~ 60% for day time ozone. The significant contributions from non-Delhi sources indicates that in Delhi (as has been show elsewhere) these strategies will also need a more regional perspective.

NCEP Air Quality Forecast(AQF) Graphics

Science.gov Websites

NAM-CMAQ Experimental Run predictions 00 03 06 09 12 15 18 21 24 27 30 33 36 39 42 45 48 Select experimental bias correction predictions NAM vs Nest forecasts Change Variable Type: Hourly CMAQ Forecasts

ENHANCED AIR POLLUTION EPIDEMIOLOGY USING A SOURCE-ORIENTED CHEMICAL TRANSPORT MODEL

EPA Science Inventory

Air quality model predictions describing source-oriented PM component concentrations in multiple size cuts will provide new inputs to examine the effects of acute and chronic PM exposure on mortality and morbidity. Associations between adverse health effects and PM sources/com...

Assimilation of Quality Controlled AIRS Temperature Profiles using the NCEP GFS

NASA Technical Reports Server (NTRS)

Susskind, Joel; Reale, Oreste; Iredell, Lena; Rosenberg, Robert

2013-01-01

We have previously conducted a number of data assimilation experiments using AIRS Version-5 quality controlled temperature profiles as a step toward finding an optimum balance of spatial coverage and sounding accuracy with regard to improving forecast skill. The data assimilation and forecast system we used was the Goddard Earth Observing System Model , Version-5 (GEOS-5) Data Assimilation System (DAS), which represents a combination of the NASA GEOS-5 forecast model with the National Centers for Environmental Prediction (NCEP) operational Grid Point Statistical Interpolation (GSI) global analysis scheme. All analyses and forecasts were run at a 0.5deg x 0.625deg spatial resolution. Data assimilation experiments were conducted in four different seasons, each in a different year. Three different sets of data assimilation experiments were run during each time period: Control; AIRS T(p); and AIRS Radiance. In the "Control" analysis, all the data used operationally by NCEP was assimilated, but no AIRS data was assimilated. Radiances from the Aqua AMSU-A instrument were also assimilated operationally by NCEP and are included in the "Control". The AIRS Radiance assimilation adds AIRS observed radiance observations for a select set of channels to the data set being assimilated, as done operationally by NCEP. In the AIRS T(p) assimilation, all information used in the Control was assimilated as well as Quality Controlled AIRS Version-5 temperature profiles, i.e., AIRS T(p) information was substituted for AIRS radiance information. The AIRS Version-5 temperature profiles were presented to the GSI analysis as rawinsonde profiles, assimilated down to a case-by-case appropriate pressure level p(sub best) determined using the Quality Control procedure. Version-5 also determines case-by-case, level-by-level error estimates of the temperature profiles, which were used as the uncertainty of each temperature measurement. These experiments using GEOS-5 have shown that forecasts resulting from analyses using the AIRS T(p) assimilation system were superior to those from the Radiance assimilation system, both with regard to global 7 day forecast skill and also the ability to predict storm tracks and intensity.

Impact of the 2008 Global Recession on Air Quality over the United States: Implications for Surface Ozone Levels from Changes in NOx Emissions

NASA Technical Reports Server (NTRS)

Tong, Daniel; Pan, Li; Chen, Weiwei; Lamsal, Lok; Lee, Pius; Tang, Youhua; Kim, Hyuncheol; Kondragunta, Shobha; Stajner, Ivanka

2016-01-01

Satellite and ground observations detected large variability in nitrogen oxides (NOx) during the 2008 economic recession, but the impact of the recession on air quality has not been quantified. This study combines observed NOx trends and a regional chemical transport model to quantify the impact of the recession on surface ozone (O3) levels over the continental United States. The impact is quantified by simulating O3 concentrations under two emission scenarios: business-as-usual (BAU) and recession. In the BAU case, the emission projection from the Cross-State Air Pollution Rule is used to estimate the would-be NOx emission level in 2011. In the recession case, the actual NO2 trends observed from Air Quality System ground monitors and the Ozone Monitoring Instrument on the Aura satellite are used to obtain realistic changes in NOx emissions. The model prediction with the recession effect agrees better with ground O3 observations over time and space than the prediction with the BAU emission. The results show that the recession caused a 12ppbv decrease in surface O3 concentration over the eastern United States, a slight increase (0.51ppbv) over the Rocky Mountain region, and mixed changes in the Pacific West. The gain in air quality benefits during the recession, however, could be quickly offset by the much slower emission reduction rate during the post-recession period.

Towards the Next Generation Air Quality Modeling System ...

EPA Pesticide Factsheets

The community multiscale air quality (CMAQ) model of the U.S. Environmental Protection Agency is one of the most widely used air quality model worldwide; it is employed for both research and regulatory applications at major universities and government agencies for improving understanding of the formation and transport of air pollutants. It is noted, however, that air quality issues and climate change assessments need to be addressed globally recognizing the linkages and interactions between meteorology and atmospheric chemistry across a wide range of scales. Therefore, an effort is currently underway to develop the next generation air quality modeling system (NGAQM) that will be based on a global integrated meteorology and chemistry system. The model for prediction across scales-atmosphere (MPAS-A), a global fully compressible non-hydrostatic model with seamlessly refined centroidal Voronoi grids, has been chosen as the meteorological driver of this modeling system. The initial step of adapting MPAS-A for the NGAQM was to implement and test the physics parameterizations and options that are preferred for retrospective air quality simulations (see the work presented by R. Gilliam, R. Bullock, and J. Herwehe at this workshop). The next step, presented herein, would be to link the chemistry from CMAQ to MPAS-A to build a prototype for the NGAQM. Furthermore, the techniques to harmonize transport processes between CMAQ and MPAS-A, methodologies to connect the chemis

The Economic Value of Air Quality Forecasting

NASA Astrophysics Data System (ADS)

Anderson-Sumo, Tasha

Both long-term and daily air quality forecasts provide an essential component to human health and impact costs. According the American Lung Association, the estimated current annual cost of air pollution related illness in the United States, adjusted for inflation (3% per year), is approximately $152 billion. Many of the risks such as hospital visits and morality are associated with poor air quality days (where the Air Quality Index is greater than 100). Groups such as sensitive groups become more susceptible to the resulting conditions and more accurate forecasts would help to take more appropriate precautions. This research focuses on evaluating the utility of air quality forecasting in terms of its potential impacts by building on air quality forecasting and economical metrics. Our analysis includes data collected during the summertime ozone seasons between 2010 and 2012 from air quality models for the Washington, DC/Baltimore, MD region. The metrics that are relevant to our analysis include: (1) The number of times that a high ozone or particulate matter (PM) episode is correctly forecasted, (2) the number of times that high ozone or PM episode is forecasted when it does not occur and (3) the number of times when the air quality forecast predicts a cleaner air episode when the air was observed to have high ozone or PM. Our collection of data included available air quality model forecasts of ozone and particulate matter data from the U.S. Environmental Protection Agency (EPA)'s AIRNOW as well as observational data of ozone and particulate matter from Clean Air Partners. We evaluated the performance of the air quality forecasts with that of the observational data and found that the forecast models perform well for the Baltimore/Washington region and the time interval observed. We estimate the potential amount for the Baltimore/Washington region accrues to a savings of up to 5,905 lives and 5.9 billion dollars per year. This total assumes perfect compliance with bad air quality warning and forecast air quality forecasts. There is a difficulty presented with evaluating the economic utility of the forecasts. All may not comply and even with a low compliance rate of 5% and 72% as the average probability of detection of poor air quality days by the air quality models, we estimate that the forecasting program saves 412 lives or 412 million dollars per year for the region. The totals we found are great or greater than other typical yearly meteorological hazard programs such as tornado or hurricane forecasting and it is clear that the economic value of air quality forecasting in the Baltimore/Washington region is vital.

Monitoring and analysis of air quality in Riga

NASA Astrophysics Data System (ADS)

Ubelis, Arnolds; Leitass, Andris; Vitols, Maris

1995-09-01

Riga, the capital of Latvia is a city with nearly 900,000 inhabitants and various highly concentrated industries. Air pollution in Riga is a serious problem affecting health and damaging valuable buildings of historical importance, as acid rain and smog take their toll. Therefore the Air Quality Management System with significant assistance from Swedish Government and persistent efforts from Riga City Council was arranged in Riga. It contains INDIC AIRVIRO system which simulates and evaluates air pollution levels at various locations. It then processes the data in order to predict air quality based on a number of criteria and parameters, measured by OPSIS differential absorption instruments, as well as data from the Meteorological Service and results of episodic measurements. The analysis of the results provided by Riga Air Quality Management System for the first time allows us to start comprehensive supervision of troposphere physical, chemical, and photochemical processes in the air of Riga as well as to appreciate the influence of lcoal pollution and transboundary transfer. The report contains the actual results of this work and first attempts of analysis as well as overview about activities towards research and teaching in the fields of spectroscopy and photochemistry of polluted atmospheres.

Remote Sensing and Spatial Growth Modeling Coupled with Air Quality Modeling to Assess the Impact of Atlanta, Georgia on the Local and Regional Environment

NASA Technical Reports Server (NTRS)

Quattrochi, Dale A.; Estes, Maurice G., Jr.; Crosson, William; Khan, Maudood

2006-01-01

The growth of cities, both in population and areal extent, appears as an inexorable process. Urbanization continues at a rapid rate, and it is estimated that by the year 2025, 80 percent of the world s population will live in cities. Directly aligned with the expansion of cities is urban sprawl. Urban expansion has profound impacts on a host of biophysical, environmental, and atmospheric processes. A reduction in air quality over cities is a major result of these impacts. Strategies that can be directly or indirectly implemented to help remediate air quality problems in cities and that can be accepted by political decision makers and the general public are now being explored to help bring down air pollutants and improve air quality. The urban landscape is inherently complex and this complexity is not adequately captured in air quality models, particularly the Community Multiscale Air Quality (CMAQ) model that is used to assess whether urban areas are in attainment of EPA air quality standards, primarily for ground level ozone. This inadequacy of the CMAQ model to sufficiently respond to the heterogeneous nature of the urban landscape can impact how well the model predicts ozone pollutant levels over metropolitan areas and ultimately, whether cities exceed EPA ozone air quality standards. We are exploring the utility of high-resolution remote sensing data and urban spatial growth modeling (SGM) projections as improved inputs to the meteorology component of the CMAQ model focusing on the Atlanta, Georgia metropolitan area as a case study. These growth projections include "business as usual" and "smart growth" scenarios out to 2030. The growth projections illustrate the effects of employing urban heat island mitigation strategies, such as increasing tree canopy and albedo across the Atlanta metro area, which in turn, are used to model how ozone and air temperature can potentially be moderated as impacts on elevating ground-level ozone, as opposed to not utilizing heat island mitigation strategies. The National Land Cover Dataset at 30m resolution is being used as the land use/land cover input and aggregated to the 4km scale for the MM5 mesoscale meteorological model and the (CMAQ) modeling schemes. Use of these data have been found to better characterize low density/suburban development as compared with USGS 1km land use/land cover data that have traditionally been used in modeling. Air quality prediction for future scenarios to 2030 is being facilitated by land use projections using a spatial growth model. Land use projections were developed using the 2030 Regional Transportation Plan developed by the Atlanta Regional Commission, the regional planning agency for the area. This allows the State Environmental Protection agency to evaluate how these transportation plans will affect future air quality. The coupled SGM and air quality modeling approach provides insight on what the impacts of Atlanta s growth will be on the local and regional environment and exists as a mechanism that can be used by policy makers to make rationale decisions on urban growth and sustainability for the metropolitan area in the future.

A GIS-based multi-source and multi-box modeling approach (GMSMB) for air pollution assessment--a North American case study.

PubMed

Wang, Bao-Zhen; Chen, Zhi

2013-01-01

This article presents a GIS-based multi-source and multi-box modeling approach (GMSMB) to predict the spatial concentration distributions of airborne pollutant on local and regional scales. In this method, an extended multi-box model combined with a multi-source and multi-grid Gaussian model are developed within the GIS framework to examine the contributions from both point- and area-source emissions. By using GIS, a large amount of data including emission sources, air quality monitoring, meteorological data, and spatial location information required for air quality modeling are brought into an integrated modeling environment. It helps more details of spatial variation in source distribution and meteorological condition to be quantitatively analyzed. The developed modeling approach has been examined to predict the spatial concentration distribution of four air pollutants (CO, NO(2), SO(2) and PM(2.5)) for the State of California. The modeling results are compared with the monitoring data. Good agreement is acquired which demonstrated that the developed modeling approach could deliver an effective air pollution assessment on both regional and local scales to support air pollution control and management planning.

Contaminant Permeation in the Ionomer-Membrane Water Processor (IWP) System

NASA Technical Reports Server (NTRS)

Kelsey, Laura K.; Finger, Barry W.; Pasadilla, Patrick; Perry, Jay

2016-01-01

The Ionomer-membrane Water Processor (IWP) is a patented membrane-distillation based urine brine water recovery system. The unique properties of the IWP membrane pair limit contaminant permeation from the brine to the recovered water and purge gas. A paper study was conducted to predict volatile trace contaminant permeation in the IWP system. Testing of a large-scale IWP Engineering Development Unit (EDU) with urine brine pretreated with the International Space Station (ISS) pretreatment formulation was then conducted to collect air and water samples for quality analysis. Distillate water quality and purge air GC-MS results are presented and compared to predictions, along with implications for the IWP brine processing system.

Application of uncertainty and sensitivity analysis to the air quality SHERPA modelling tool

NASA Astrophysics Data System (ADS)

Pisoni, E.; Albrecht, D.; Mara, T. A.; Rosati, R.; Tarantola, S.; Thunis, P.

2018-06-01

Air quality has significantly improved in Europe over the past few decades. Nonetheless we still find high concentrations in measurements mainly in specific regions or cities. This dimensional shift, from EU-wide to hot-spot exceedances, calls for a novel approach to regional air quality management (to complement EU-wide existing policies). The SHERPA (Screening for High Emission Reduction Potentials on Air quality) modelling tool was developed in this context. It provides an additional tool to be used in support to regional/local decision makers responsible for the design of air quality plans. It is therefore important to evaluate the quality of the SHERPA model, and its behavior in the face of various kinds of uncertainty. Uncertainty and sensitivity analysis techniques can be used for this purpose. They both reveal the links between assumptions and forecasts, help in-model simplification and may highlight unexpected relationships between inputs and outputs. Thus, a policy steered SHERPA module - predicting air quality improvement linked to emission reduction scenarios - was evaluated by means of (1) uncertainty analysis (UA) to quantify uncertainty in the model output, and (2) by sensitivity analysis (SA) to identify the most influential input sources of this uncertainty. The results of this study provide relevant information about the key variables driving the SHERPA output uncertainty, and advise policy-makers and modellers where to place their efforts for an improved decision-making process.

The Improvement of Spatial-Temporal PM2.5 Resolution in Taiwan by Using Data Assimilation Method

NASA Astrophysics Data System (ADS)

Lin, Yong-Qing; Lin, Yuan-Chien

2017-04-01

Forecasting air pollution concentration, e.g., the concentration of PM2.5, is of great significance to protect human health and the environment. Accurate prediction of PM2.5 concentrations is limited in number and the data quality of air quality monitoring stations. The spatial and temporal variations of PM2.5 concentrations are measured by 76 National Air Quality Monitoring Stations (built by the TW-EPA) in Taiwan. The National Air Quality Monitoring Stations are costly and scarce because of the highly precise instrument and their size. Therefore, many places still out of the range of National Air Quality Monitoring Stations. Recently, there are an enormous number of portable air quality sensors called "AirBox" developed jointly by the Taiwan government and a private company. By virtue of its price and portative, the AirBox can provide higher resolution of space-time PM2.5 measurement. However, the spatiotemporal distribution and data quality are different between AirBox and National Air Quality Monitoring Stations. To integrate the heterogeneous PM2.5 data, the data assimilation method should be performed before further analysis. In this study, we propose a data assimilation method based on Ensemble Kalman Filter (EnKF), which is a variant of classic Kalman Filter, can be used to combine additional heterogeneous data from different source while modeling to improve the estimation of spatial-temporal PM2.5 concentration. The assimilation procedure uses the advantages of the two kinds of heterogeneous data and merges them to produce the final estimation. The results have shown that by combining AirBox PM2.5 data as additional information in our model based EnKF can bring the better estimation of spatial-temporal PM2.5 concentration and improve the it's space-time resolution. Under the approach proposed in this study, higher spatial-temporal resoultion could provide a very useful information for a better spatial-temporal data analysis and further environmental management, such as air pollution source localization and micro-scale air pollution analysis. Keywords: PM2.5, Data Assimilation, Ensemble Kalman Filter, Air Quality

Operational air quality forecast guidance for the United States

NASA Astrophysics Data System (ADS)

Stajner, Ivanka; Lee, Pius; Tong, Daniel; Pan, Li; McQueen, Jeff; Huang, Jinaping; Djalalova, Irina; Wilczak, James; Huang, Ho-Chun; Wang, Jun; Stein, Ariel; Upadhayay, Sikchya

2016-04-01

NOAA provides operational air quality predictions for ozone and wildfire smoke over the United States (U.S.) and predictions of airborne dust over the contiguous 48 states at http://airquality.weather.gov. These predictions are produced using U.S. Environmental Protection Agency (EPA) Community Model for Air Quality (CMAQ) and NOAA's HYSPLIT model (Stein et al., 2015) with meteorological inputs from the North American Mesoscale Forecast System (NAM). The current efforts focus on improving test predictions of fine particulate matter (PM2.5) from CMAQ. Emission inputs for ozone and PM2.5 predictions include inventory information from the U.S. EPA and recently added contributions of particulate matter from intermittent wildfires and windblown dust that rely on near real-time information. Current testing includes refinement of the vertical grid structure in CMAQ and inclusion of contributions of dust transport from global sources into the U.S. domain using the NEMS Global Aerosol Capability (NGAC). The addition of wildfire smoke and dust contributions in CMAQ reduced model underestimation of PM2.5 in summertime. Wintertime overestimation of PM2.5 was reduced by suppressing emissions of soil particles when the terrain is covered by snow or ice. Nevertheless, seasonal biases and biases in the diurnal cycle of PM2.5 are still substantial. Therefore, a new bias correction procedure based on an analog ensemble approach was introduced (Djalalova et al., 2015). It virtually eliminates biases in monthly means or in the diurnal cycle, but it also reduces day-to-day variability in PM2.5 predictions. Refinements to the bias correction procedure are being developed. Upgrades for the representation of wildfire smoke emissions within the domain and from global sources are in testing. Another area of active development includes approaches to scale emission inventories for nitrogen oxides in order to reproduce recent changes observed by the AirNow surface monitoring network and by satellite instruments (Tong et al., 2015) and to use these updated emissions to improve ozone predictions (Pan et al., 2015). An overview of the impacts of these recent and ongoing efforts to improve predictions of ozone, smoke and PM2.5 will be presented. Djalalova, I. et al., 2015: PM2.5 analog forecast and Kalman filter post-processing for the Community Multiscale Air Quality (CMAQ) model. Atmospheric Environment, doi:10.1016/j.atmosenv.2015.05.057. Pan L. et al., 2014: Assessment of NOx and O3 forecasting performances in the U.S. National Air Quality Forecasting Capability before and after the 2012 major emissions updates. Atmospheric Environment, doi: 10.1016/j.atmosenv.2014.06.020. Stein, A. et al., 2015: NOAA's HYSPLIT atmospheric transport and dispersion modeling system. Bull. Amer. Meteor. Soc., doi:10.1175/BAMS-D-14-00110.1. Tong, D.Q. et al., 2015: Long-term NOx trends over large cities in the United States during the great recession: Comparison of satellite retrievals, ground observations, and emission inventories. Atmospheric Environment, doi:10.1016/j.atmosenv.2015.01.035.

Enforcement of continuous compliance with air quality regulations

DOE Office of Scientific and Technical Information (OSTI.GOV)

Harrington, C.W.

1985-01-01

The compliance of stationary air-pollution sources with air quality regulations is examined. Contrary to the predictions of economic models of enforcement, sources are generally in continuous compliance with the regulations. An alternative voluntary compliance model of enforcement is proposed, in which regulated sources are penalized not for violations, but for failing to return to compliance when a violation is discovered. It is argued that continuous compliance is a bargain struck between the source and the regulatory agency, in which the agency agrees to avoid use of penalties in return for the source's good faith attempts to maintain compliance.

Development and Validation of a Predictive Model to Assess the Impact of Coastal Operations On Urban Scale Air Quality

DTIC Science & Technology

2006-09-29

originating in Los Angeles. The long ridge of ozone in the north east part of Figure 14a is due to polluted air from San Diego area that has undergone...Further north of this small ridge (2 ppbv), we find a decrease in O3 of up to 6ppb (i.e., without DoD emissions, O3 is reduced by 6ppb). This period is...ships on urban air quality. 35 6.0 References Alexis, A., P. Gaffney , C. Garcia, M. Nystrom, and R. Rood (2000), The 1999 California Almanac of

Improved Prediction Models For PCC Pavement Performance-Related Specifications, Volume I: Final Report

DOT National Transportation Integrated Search

1992-01-01

This brochure summarizes the provisions of the Intermodal Surface Transportation Efficiency Act of 1991 (ISTEA) that can best help State and local officials as they work toward the air quality goals of the Clean Air Act Amendments of 1990 (CAAA). Thi...

Geostatistical uncertainty of assessing air quality using high-spatial-resolution lichen data: A health study in the urban area of Sines, Portugal.

PubMed

Ribeiro, Manuel C; Pinho, P; Branquinho, C; Llop, Esteve; Pereira, Maria J

2016-08-15

In most studies correlating health outcomes with air pollution, personal exposure assignments are based on measurements collected at air-quality monitoring stations not coinciding with health data locations. In such cases, interpolators are needed to predict air quality in unsampled locations and to assign personal exposures. Moreover, a measure of the spatial uncertainty of exposures should be incorporated, especially in urban areas where concentrations vary at short distances due to changes in land use and pollution intensity. These studies are limited by the lack of literature comparing exposure uncertainty derived from distinct spatial interpolators. Here, we addressed these issues with two interpolation methods: regression Kriging (RK) and ordinary Kriging (OK). These methods were used to generate air-quality simulations with a geostatistical algorithm. For each method, the geostatistical uncertainty was drawn from generalized linear model (GLM) analysis. We analyzed the association between air quality and birth weight. Personal health data (n=227) and exposure data were collected in Sines (Portugal) during 2007-2010. Because air-quality monitoring stations in the city do not offer high-spatial-resolution measurements (n=1), we used lichen data as an ecological indicator of air quality (n=83). We found no significant difference in the fit of GLMs with any of the geostatistical methods. With RK, however, the models tended to fit better more often and worse less often. Moreover, the geostatistical uncertainty results showed a marginally higher mean and precision with RK. Combined with lichen data and land-use data of high spatial resolution, RK is a more effective geostatistical method for relating health outcomes with air quality in urban areas. This is particularly important in small cities, which generally do not have expensive air-quality monitoring stations with high spatial resolution. Further, alternative ways of linking human activities with their environment are needed to improve human well-being. Copyright © 2016 Elsevier B.V. All rights reserved.

Characterization of Emissions and Air Quality Modeling for Predicting the Impacts of Prescribed Burns at DoD Lands

DTIC Science & Technology

2012-10-01

Sum of NOx and its oxidation products NOAA National Oceanic and Atmospheric Administration O3 Ozone OAQPS Office of Air Quality Planning and...Emission related parameters such as emission strength, timing, and vertical distribution (plume fraction penetrating into the free troposphere ) proved to...emissions and the effect of PB on ozone levels in Columbus-Phenix City metropolitan areas are also of concern. Forest fires produce nitrogen oxides

Spatial Growth Modeling and High Resolution Remote Sensing Data Coupled with Air Quality Modeling to Assess the Impact of Atlanta, Georgia on the Local and Regional Environment

NASA Technical Reports Server (NTRS)

Quattrochi, Dale A.; Estes, Maurice G., Jr.; Crosson, William; Johnson, Hoyt; Khan, Maudood

2006-01-01

The growth of cities, both in population and areal extent, appears as an inexorable process. Urbanization continues at a rapid rate, and it is estimated that by the year 2025, 60 percent of the world s population will live in cities. Urban expansion has profound impacts on a host of biophysical, environmental, and atmospheric processes within an urban ecosystems perspective. A reduction in air quality over cities is a major result of these impacts. Because of its complexity, the urban landscape is not adequately captured in air quality models such as the Community Multiscale Air Quality (CMAQ) model that is used to assess whether urban areas are in attainment of EPA air quality standards, primarily for ground level ozone. This inadequacy of the CMAQ model to sufficiently respond to the heterogeneous nature of the urban landscape can impact how well the model predicts ozone levels over metropolitan areas and ultimately, whether cities exceed EPA ozone air quality standards. We are exploring the utility of high-resolution remote sensing data and urban spatial growth modeling (SGM) projections as improved inputs to a meteorological/air quality modeling system focusing on the Atlanta, Georgia metropolitan area as a case study. These growth projections include business as usual and smart growth scenarios out to 2030. The growth projections illustrate the effects of employing urban heat island mitigation strategies, such as increasing tree canopy and albedo across the Atlanta metro area, which in turn, are used to model how air temperature can potentially be moderated as impacts on elevating ground-level ozone, as opposed to not utilizing heat island mitigation strategies. The National Land Cover Dataset at 30m resolution is being used as the land use/land cover input and aggregated to the 4km scale for the MM5 mesoscale meteorological model and the CMAQ modeling schemes. Use of these data has been found to better characterize low density/suburban development as compared with USGS lkm land use/land cover data that have traditionally been used in modeling. Air quality prediction for future scenarios to 2030 is being facilitated by land use projections using a spatial growth model. Land use projections were developed using the 2030 Regional Transportation Plan developed by the Atlanta Regional Commission, the regional planning agency for the area. This allows the Georgia Environmental Protection Division to evaluate how these transportation plans will affect future air quality. The coupled SGM and air quality modeling approach provides insight on what the impacts of Atlanta s growth will be on the local and regional environment and exists as a mechanism that can be used by policy makers to make rational decisions on urban growth and sustainability for the metropolitan area in the future.

Remote Sensing and Spatial Growth Modeling Coupled With Air Quality Modeling to Assess the Impact of Atlanta, Georgia on the Local and Regional Environment

NASA Astrophysics Data System (ADS)

Quattrochi, D. A.; Estes, M. G.; Crosson, W. L.; Johnson, H.; Khan, M.

2006-05-01

The growth of cities, both in population and areal extent, appears as an inexorable process. Urbanization continues at a rapid rate, and it is estimated that by the year 2025, 60 percent of the world's population will live in cities. Urban expansion has profound impacts on a host of biophysical, environmental, and atmospheric processes within an urban ecosystems perspective. A reduction in air quality over cities is a major result of these impacts. Because of its complexity, the urban landscape is not adequately captured in air quality models such as the Community Multiscale Air Quality (CMAQ) model that is used to assess whether urban areas are in attainment of EPA air quality standards, primarily for ground level ozone. This inadequacy of the CMAQ model to sufficiently respond to the heterogeneous nature of the urban landscape can impact how well the model predicts ozone levels over metropolitan areas and ultimately, whether cities exceed EPA ozone air quality standards. We are exploring the utility of high-resolution remote sensing data and urban spatial growth modeling (SGM) projections as improved inputs to a meteorological/air quality modeling system focusing on the Atlanta, Georgia metropolitan area as a case study. These growth projections include "business as usual" and "smart growth" scenarios out to 2030. The growth projections illustrate the effects of employing urban heat island mitigation strategies, such as increasing tree canopy and albedo across the Atlanta metro area, which in turn, are used to model how air temperature can potentially be moderated as impacts on elevating ground-level ozone, as opposed to not utilizing heat island mitigation strategies. The National Land Cover Dataset at 30m resolution is being used as the land use/land cover input and aggregated to the 4km scale for the MM5 mesoscale meteorological model and the CMAQ modeling schemes. Use of these data has been found to better characterize low density/suburban development as compared with USGS 1km land use/land cover data that have traditionally been used in modeling. Air quality prediction for future scenarios to 2030 is being facilitated by land use projections using a spatial growth model. Land use projections were developed using the 2030 Regional Transportation Plan developed by the Atlanta Regional Commission, the regional planning agency for the area. This allows the Georgia Environmental Protection Division to evaluate how these transportation plans will affect future air quality. The coupled SGM and air quality modeling approach provides insight on what the impacts of Atlanta's growth will be on the local and regional environment and exists as a mechanism that can be used by policy makers to make rational decisions on urban growth and sustainability for the metropolitan area in the future.

Role of future scenarios in understanding deep uncertainty in long-term air quality management.

PubMed

Gamas, Julia; Dodder, Rebecca; Loughlin, Dan; Gage, Cynthia

2015-11-01

The environment and its interactions with human systems, whether economic, social, or political, are complex. Relevant drivers may disrupt system dynamics in unforeseen ways, making it difficult to predict future conditions. This kind of "deep uncertainty" presents a challenge to organizations faced with making decisions about the future, including those involved in air quality management. Scenario Planning is a structured process that involves the development of narratives describing alternative future states of the world, designed to differ with respect to the most critical and uncertain drivers. The resulting scenarios are then used to understand the consequences of those futures and to prepare for them with robust management strategies. We demonstrate a novel air quality management application of Scenario Planning. Through a series of workshops, important air quality drivers were identified. The most critical and uncertain drivers were found to be "technological development" and "change in societal paradigms." These drivers were used as a basis to develop four distinct scenario storylines. The energy and emissions implications of each storyline were then modeled using the MARKAL energy system model. NOx emissions were found to decrease for all scenarios, largely a response to existing air quality regulations, whereas SO2 emissions ranged from 12% greater to 7% lower than 2015 emissions levels. Future-year emissions differed considerably from one scenario to another, however, with key differentiating factors being transition to cleaner fuels and energy demand reductions. Application of scenarios in air quality management provides a structured means of sifting through and understanding the dynamics of the many complex driving forces affecting future air quality. Further, scenarios provide a means to identify opportunities and challenges for future air quality management, as well as a platform for testing the efficacy and robustness of particular management options across wide-ranging conditions.

Improving Air Quality (and Weather) Predictions using Advanced Data Assimilation Techniques Applied to Coupled Models during KORUS-AQ

NASA Astrophysics Data System (ADS)

Carmichael, G. R.; Saide, P. E.; Gao, M.; Streets, D. G.; Kim, J.; Woo, J. H.

2017-12-01

Ambient aerosols are important air pollutants with direct impacts on human health and on the Earth's weather and climate systems through their interactions with radiation and clouds. Their role is dependent on their distributions of size, number, phase and composition, which vary significantly in space and time. There remain large uncertainties in simulated aerosol distributions due to uncertainties in emission estimates and in chemical and physical processes associated with their formation and removal. These uncertainties lead to large uncertainties in weather and air quality predictions and in estimates of health and climate change impacts. Despite these uncertainties and challenges, regional-scale coupled chemistry-meteorological models such as WRF-Chem have significant capabilities in predicting aerosol distributions and explaining aerosol-weather interactions. We explore the hypothesis that new advances in on-line, coupled atmospheric chemistry/meteorological models, and new emission inversion and data assimilation techniques applicable to such coupled models, can be applied in innovative ways using current and evolving observation systems to improve predictions of aerosol distributions at regional scales. We investigate the impacts of assimilating AOD from geostationary satellite (GOCI) and surface PM2.5 measurements on predictions of AOD and PM in Korea during KORUS-AQ through a series of experiments. The results suggest assimilating datasets from multiple platforms can improve the predictions of aerosol temporal and spatial distributions.

Postprocessing for Air Quality Predictions

NASA Astrophysics Data System (ADS)

Delle Monache, L.

2017-12-01

In recent year, air quality (AQ) forecasting has made significant progress towards better predictions with the goal of protecting the public from harmful pollutants. This progress is the results of improvements in weather and chemical transport models, their coupling, and more accurate emission inventories (e.g., with the development of new algorithms to account in near real-time for fires). Nevertheless, AQ predictions are still affected at times by significant biases which stem from limitations in both weather and chemistry transport models. Those are the result of numerical approximations and the poor representation (and understanding) of important physical and chemical process. Moreover, although the quality of emission inventories has been significantly improved, they are still one of the main sources of uncertainties in AQ predictions. For operational real-time AQ forecasting, a significant portion of these biases can be reduced with the implementation of postprocessing methods. We will review some of the techniques that have been proposed to reduce both systematic and random errors of AQ predictions, and improve the correlation between predictions and observations of ground-level ozone and surface particulate matter less than 2.5 µm in diameter (PM2.5). These methods, which can be applied to both deterministic and probabilistic predictions, include simple bias-correction techniques, corrections inspired by the Kalman filter, regression methods, and the more recently developed analog-based algorithms. These approaches will be compared and contrasted, and strength and weaknesses of each will be discussed.

AMMONIA EMISSIONS AND THEIR IMPLICATIONS ON FINE PARTICULATE MATTER FORMATION IN NORTH CAROLINA

EPA Science Inventory

Ammonia (NH₃) is an important atmospheric pollutant that plays a key role in several air pollution problems. The accuracy of NH₃ emissions can have a large effect on air quality model (AQM) predictions of aerosol sulfate, nitrate, and ammonium concentration...

Simultaneous statistical bias correction of multiplePM2.5 species from a regional photochemical grid model

EPA Science Inventory

In recent years environmental epidemiologists have begun utilizing regionalscale air quality computer models to predict ambient air pollution concentrations in health studies instead of or in addition to monitoring data from central sites. The advantages of using such models i...

Predicting the Effects of Nano-Scale Cerium Additives in Diesel Fuel on Regional-Scale Air Quality

EPA Science Inventory

Diesel vehicles are a major source of air pollutant emissions. Fuel additives containing nanoparticulate cerium (nCe) are currently being used in some diesel vehicles to improve fuel efficiency. These fuel additives also reduce fine particulate matter (PM_2.5) emissio...

A REGIONAL MODEL FOR PCDD/F'S BASED ON A PHOTOCHEMICAL MODEL FOR AIR QUALITY AND PARTICULATE MATTER

EPA Science Inventory

How important is gas to particle partitioning in predicting air concentrations and deposition of Poly-Chlorinated Dibenzo-p-Dioxins and Furans (PCDD/F's)? Literature indicates that the fate of emissions changes because the summation of atmospheric processes has a different balanc...

Combining Regional- and Local-Scale Air Quality Models with Exposure Models for Use in Environmental Health Studies

EPA Science Inventory

Population-based human exposure models predict the distribution of personal exposures to pollutants of outdoor origin using a variety of inputs, including: air pollution concentrations; human activity patterns, such as the amount of time spent outdoors vs. indoors, commuting, wal...

CALIPSO Satellite Lidar Identification Of Elevated Dust Over Australia Compared With Air Quality Model PM60 Forecasts

NASA Technical Reports Server (NTRS)

Young, Stuart A.; Vaughan, Mark; Omar, Ali; Liu, Zhaoyan; Lee, Sunhee; Hu, Youngxiang; Cope, Martin

2008-01-01

Global measurements of the vertical distribution of clouds and aerosols have been recorded by the lidar on board the CALIPSO (Cloud Aerosol Lidar Infrared Pathfinder Satellite Observations) satellite since June 2006. Such extensive, height-resolved measurements provide a rare and valuable opportunity for developing, testing and validating various atmospheric models, including global climate, numerical weather prediction, chemical transport and air quality models. Here we report on the initial results of an investigation into the performance of the Australian Air Quality Forecast System (AAQFS) model in forecasting the distribution of elevated dust over the Australian region. The model forecasts of PM60 dust distribution are compared with the CALIPSO lidar Vertical Feature Mask (VFM) data product. The VFM classifies contiguous atmospheric regions of enhanced backscatter as either cloud or aerosols. Aerosols are further classified into six subtypes. By comparing forecast PM60 concentration profiles to the spatial distribution of dust reported in the CALIPSO VFM, we can assess the model s ability to predict the occurrence and the vertical and horizontal extents of dust events within the study area.

Assessing the Association Between Asthma and Air Quality in the Presence of Wildfires

NASA Technical Reports Server (NTRS)

Young, L. J.; Lopiano, K. K.; Xu, X.; Holt, N. M.; Leary, E.; Al-Hamdan, M. Z.; Crosson, W. L.; Estes, M. G.; Luvall, J. C.; Estes, S. M.;

Wavelength dependent light absorption as a cost effective, real-time surrogate for ambient concentrations of polycyclic aromatic hydrocarbons

NASA Astrophysics Data System (ADS)

Brown, Richard J. C.; Butterfield, David M.; Goddard, Sharon L.; Hussain, Delwar; Quincey, Paul G.; Fuller, Gary W.

2016-02-01

Many monitoring stations used to assess ambient air concentrations of pollutants regulated by European air quality directives suffer from being expensive to establish and operate, and from their location being based on the results of macro-scale modelling exercises rather than measurement assessments in candidate locations. To address these issues for the monitoring of polycyclic aromatic hydrocarbons (PAHs), this study has used data from a combination of the ultraviolet and infrared channels of aethalometers (referred to as UV BC), operated as part of the UK Black Carbon Network, as a surrogate measurement. This has established a relationship between concentrations of the PAH regulated in Europe, benzo[a]pyrene (B[a]P), and the UV BC signal at locations where these measurements have been made together from 2008 to 2014. This relationship was observed to be non-linear. Relationships for individual site types were used to predict measured concentrations with, on average, 1.5% accuracy across all annual averages, and with only 1 in 36 of the predicted annual averages deviating from the measured annual average by more than the B[a]P data quality objective for uncertainty of 50% (at -65%, with the range excluding this value between + 38% and -37%). These relationships were then used to predict B[a]P concentrations at stations where UV BC measurement are made, but PAH measurements are not. This process produced results which reflected expectations based on knowledge of the pollution climate at these stations gained from the measurements of other air quality networks, or from nearby stations. The influence of domestic solid fuel heating was clear using this approach which highlighted Strabane in Northern Ireland as a station likely to be in excess of the air quality directive target value for B[a]P.

Evaluation of DeNitrification DeComposition Model to Estimate Ammonia Fluxes from Chemical Fertilizer Application

NASA Astrophysics Data System (ADS)

Balasubramanian, S.; Nelson, A. J.; Koloutsou-Vakakis, S.; Lin, J.; Myles, L.; Rood, M. J.

2016-12-01

Biogeochemical models such as DeNitrification DeComposition (DNDC) are used to model greenhouse and other trace gas fluxes (e.g., ammonia (NH3)) from agricultural ecosystems. NH3 is of interest to air quality because it is a precursor to ambient particulate matter. NH3 fluxes from chemical fertilizer application are uncertain due to dependence on local weather and soil properties, and farm nitrogen management practices. DNDC can be advantageously implemented to model the underlying spatial and temporal trends to support air quality modeling. However, such implementation, requires a detailed evaluation of model predictions, and model behavior. This is the first study to assess DNDC predictions of NH3 fluxes to/from the atmosphere, from chemical fertilizer application, during an entire crop growing season, in the United States. Relaxed eddy accumulation (REA) measurements over corn in Central Illinois, in year 2014, were used to evaluate magnitude and trends in modeled NH3 fluxes. DNDC was able to replicate both magnitude and trends in measured NH3 fluxes, with greater accuracy during the initial 33 days after application, when NH3 was mostly emitted to the atmosphere. However, poorer performance was observed when depositional fluxes were measured. Sensitivity analysis using Monte Carlo simulations indicated that modeled NH3 fluxes were most sensitive to input air temperature and precipitation, soil organic carbon, field capacity and pH and fertilizer loading rate, timing, and application depth and tilling date. By constraining these inputs for conditions in Central Illinois, uncertainty in annual NH3 fluxes was estimated to vary from -87% to 61%. Results from this study provides insight to further improve DNDC predictions and inform efforts for upscaling site predictions to regional scale for the development of emission inventories for air quality modeling.

Assessment of hydrogen sulfide emission from a sewage treatment plant using AERMOD.

PubMed

Baawain, Mahad; Al-Mamun, Abdullah; Omidvarborna, Hamid; Al-Jabri, Abdullah

2017-06-01

Air quality modeling plays an important role in prediction of air pollutants in urban areas. Moreover, it is also an essential component to make crucial decisions in environmental management. In this study, environmental protection agency (EPA) regulatory model (AERMOD) was implemented in order to assess the urban air quality in the city of Muscat, Sultanate of Oman. Dispersion modeling was employed for the prediction of hydrogen sulfide (H 2 S) emissions, a neighborhood claimed issue, from Al-Ansab sewage treatment plant (STP). Meteorological, elevation data, and H 2 S survey results were implemented into the model. From the site survey study, four different H 2 S emission sources were identified as sewage tanker connection points, biofilter, old odor control unit (OCU), and open channels of raw sewage. It was observed that based on maximum 24-h analysis, the ground level concentration outside the STP exceeded the concentration limit, 40 μg/m 3 , recommended by the local regulating agency in Oman. By applying a sensitivity analysis study, the locations with the highest predicted H 2 S levels were identified. The most affected area in the worst-case scenario was the nearby expressway with 450.9 μg/m 3 of H 2 S. The highest ground level concentration of H 2 S was detected in March, while the lowest was measured in December. The model also predicted that the impact of odor nuisance is greater at the summer season than that of other seasons due to the elevated temperatures. The study revealed an adverse environmental impact from the STPs on urban air quality, which may pose a threat to the public health.

A method to estimate spatiotemporal air quality in an urban traffic corridor.

PubMed

Singh, Nongthombam Premananda; Gokhale, Sharad

2015-12-15

Air quality exposure assessment using personal exposure sampling or direct measurement of spatiotemporal air pollutant concentrations has difficulty and limitations. Most statistical methods used for estimating spatiotemporal air quality do not account for the source characteristics (e.g. emissions). In this study, a prediction method, based on the lognormal probability distribution of hourly-average-spatial concentrations of carbon monoxide (CO) obtained by a CALINE4 model, has been developed and validated in an urban traffic corridor. The data on CO concentrations were collected at three locations and traffic and meteorology within the urban traffic corridor.(1) The method has been developed with the data of one location and validated at other two locations. The method estimated the CO concentrations reasonably well (correlation coefficient, r≥0.96). Later, the method has been applied to estimate the probability of occurrence [P(C≥Cstd] of the spatial CO concentrations in the corridor. The results have been promising and, therefore, may be useful to quantifying spatiotemporal air quality within an urban area. Copyright © 2015 Elsevier B.V. All rights reserved.

Study of Regional Downscaled Climate and Air Quality in the United States

NASA Astrophysics Data System (ADS)

Gao, Y.; Fu, J. S.; Drake, J.; Lamarque, J.; Lam, Y.; Huang, K.

2011-12-01

Due to the increasing anthropogenic greenhouse gas emissions, the global and regional climate patterns have significantly changed. Climate change has exerted strong impact on ecosystem, air quality and human life. The global model Community Earth System Model (CESM v1.0) was used to predict future climate and chemistry under projected emission scenarios. Two new emission scenarios, Representative Community Pathways (RCP) 4.5 and RCP 8.5, were used in this study for climate and chemistry simulations. The projected global mean temperature will increase 1.2 and 1.7 degree Celcius for the RCP 4.5 and RCP 8.5 scenarios in 2050s, respectively. In order to take advantage of local detailed topography, land use data and conduct local climate impact on air quality, we downscaled CESM outputs to 4 km by 4 km Eastern US domain using Weather Research and Forecasting (WRF) Model and Community Multi-scale Air Quality modeling system (CMAQ). The evaluations between regional model outputs and global model outputs, regional model outputs and observational data were conducted to verify the downscaled methodology. Future climate change and air quality impact were also examined on a 4 km by 4 km high resolution scale.

Windblown Dust and Air Quality Under a Changing Climate in the Pacific Northwest

NASA Astrophysics Data System (ADS)

Sharratt, B. S.; Tatarko, J.; Abatzoglou, J. T.; Fox, F.; Huggins, D. R.

2016-12-01

Wind erosion is a concern for sustainable agriculture and societal health in the US Pacific Northwest. Indeed, wind erosion continues to cause exceedances of the National Ambient Air Quality Standard for PM10 in the region. Can we expect air quality to deteriorate or improve as climate changes? Will wind erosion escalate in the future under a warmer and drier climate as forecast for Australia, southern prairies of Canada, northern China, and United States Corn Belt and Colorado Plateau? To answer these questions, we used 18 global climate models, cropping systems simulation model (CropSyst), and the Wind Erosion Prediction System (WEPS) to simulate the complex interactions among climate, crop production, and wind erosion. These simulations were carried out in eastern Washington where wind erosion of agricultural lands contribute to poor air quality in the region. Our results suggest that an increase in temperature and CO2 concentration, coupled with nominal increases in precipitation, will enhance biomass production and reduce soil and PM10 losses by the mid-21st century. This study reveals that climate change may reduce the risk of wind erosion and improve air quality in the Inland Pacific Northwest.

Air quality and acute deaths in California, 2000-2012.

PubMed

Young, S Stanley; Smith, Richard L; Lopiano, Keneth K

2017-08-01

Many studies have shown an association between air quality and acute deaths, and such associations are widely interpreted as causal. Several factors call causation and even association into question, for example multiple testing and multiple modeling, publication bias and confirmation bias. Many published studies are difficult or impossible to reproduce because of lack of access to confidential data sources. Here we make publically available a dataset containing daily air quality levels, PM 2.5 and ozone, daily temperature levels, minimum and maximum and daily maximum relative humidity levels for the eight most populous California air basins, thirteen years, >2M deaths, over 37,000 exposure days. The data are analyzed using standard time series analysis, and a sensitivity analysis is computed varying model parameters, locations and years. Our analysis finds little evidence for association between air quality and acute deaths. These results are consistent with those for the widely cited NMMAPS dataset when the latter are restricted to California. The daily death variability was mostly explained by time of year or weather variables; Neither PM 2.5 nor ozone added appreciably to the prediction of daily deaths. These results call into question the widespread belief that association between air quality and acute deaths is causal/near-universal. Copyright © 2017 Elsevier Inc. All rights reserved.

Air quality modeling for effective environmental management in the mining region.

PubMed

Asif, Zunaira; Chen, Zhi; Han, Yi

2018-04-18

Air quality in the mining sector is a serious environmental concern and associated with many health issues. The air quality management in mining region has been facing many challenges due to lack of understanding of atmospheric factors and physical removal mechanism. A modeling approach called mining air dispersion model (MADM) is developed to predict air pollutants concentration in the mining region while considering the deposition effect. The model is taken into account through the planet's boundary conditions and assuming that the eddy diffusivity depends on the downwind distance. The developed MADM is applied to a mining site in Canada. The model provides values as the predicted concentrations of PM 10 , PM 2.5 , TSP, NO 2 and six heavy metals (As, Pb, Hg, Cd, Zn, Cr) at various receptor locations. The model shows that neutral stability conditions are dominant for the study site. The maximum mixing height is achieved (1280 m) during the evening of summer, and minimum mixing height (380 m) is attained during the evening of winter. The dust fall (PM coarse) deposition flux is maximum during February and March with the deposition velocity of 4.67 cm/s. The results are evaluated with the monitoring field values, revealing a good agreement for the target air pollutants with R-squared ranging from 0.72 to 0.96 for PM 2.5 ; 0.71 to 0.82 for PM 10 and from 0.71 to 0.89 for NO 2 . The analyses illustrate that presented algorithm in this model can be used to assess air quality for the mining site in a systematic way. The comparison of MADM and CALPUFF modeling values are made for four different pollutants (PM 2.5 , PM 10 , TSP, and NO 2 ) under three different atmospheric stability classes (stable, neutral and unstable). Further, MADM results are statistically tested against CALPUFF for the air pollutants and model performance is found satisfactory.

Assessment of the emissions and air quality impacts of biomass and biogas use in California.

PubMed

Carreras-Sospedra, Marc; Williams, Robert; Dabdub, Donald

2016-02-01

It is estimated that there is sufficient in-state "technically" recoverable biomass to support nearly 4000 MW of bioelectricity generation capacity. This study assesses the emissions of greenhouse gases and air pollutants and resulting air quality impacts of new and existing bioenergy capacity throughout the state of California, focusing on feedstocks and advanced technologies utilizing biomass resources predominant in each region. The options for bioresources include the production of bioelectricity and renewable natural gas (NG). Emissions of criteria pollutants and greenhouse gases are quantified for a set of scenarios that span the emission factors for power generation and the use of renewable natural gas for vehicle fueling. Emissions are input to the Community Multiscale Air Quality (CMAQ) model to predict regional and statewide temporal air quality impacts from the biopower scenarios. With current technology and at the emission levels of current installations, maximum bioelectricity production could increase nitrogen oxide (NOx) emissions by 10% in 2020, which would cause increases in ozone and particulate matter concentrations in large areas of California. Technology upgrades would achieve the lowest criteria pollutant emissions. Conversion of biomass to compressed NG (CNG) for vehicles would achieve comparable emission reductions of criteria pollutants and minimize emissions of greenhouse gases (GHG). Air quality modeling of biomass scenarios suggest that applying technological changes and emission controls would minimize the air quality impacts of bioelectricity generation. And a shift from bioelectricity production to CNG production for vehicles would reduce air quality impacts further. From a co-benefits standpoint, CNG production for vehicles appears to provide the best benefits in terms of GHG emissions and air quality. This investigation provides a consistent analysis of air quality impacts and greenhouse gas emissions for scenarios examining increased biomass use. Further work involving economic assessment, seasonal or annual emissions and air quality modeling, and potential exposure analysis would help inform policy makers and industry with respect to further development and direction of biomass policy and bioenergy technology alternatives needed to meet energy and environmental goals in California.

Regional Modelling of Air Quality in the Canadian Arctic: Impact of marine shipping and North American wild fire emissions

NASA Astrophysics Data System (ADS)

Gong, W.; Beagley, S. R.; Zhang, J.; Cousineau, S.; Sassi, M.; Munoz-Alpizar, R.; Racine, J.; Menard, S.; Chen, J.

2015-12-01

Arctic atmospheric composition is strongly influenced by long-range transport from mid-latitudes as well as processes occurring in the Arctic locally. Using an on-line air quality prediction model GEM-MACH, simulations were carried out for the 2010 northern shipping season (April - October) over a regional Arctic domain. North American wildfire emissions and Arctic shipping emissions were represented, along with other anthropogenic and biogenic emissions. Sensitivity studies were carried out to investigate the principal sources and processes affecting air quality in the Canadian Northern and Arctic regions. In this paper, we present an analysis of sources, transport, and removal processes on the ambient concentrations and atmospheric loading of various pollutants with air quality and climate implications, such as, O3, NOx, SO2, CO, and aerosols (sulfate, black carbon, and organic carbon components). Preliminary results from a model simulation of a recent summertime Arctic field campaign will also be presented.

“Summary of the Emission Inventories compiled for the ...

EPA Pesticide Factsheets

We present a summary of the emission inventories from the US, Canada, and Mexico developed for the second phase of the Air Quality Model Evaluation International Initiative (AQMEII). Activities in this second phase are focused on the application and evaluation of coupled meteorology-chemistry models over both North America and Europe using common emissions and boundary conditions for all modeling groups for the years of 2006 and 2010. We will compare the emission inventories developed for these two years focusing on the SO2 and NOx reductions over these years and compare with socio-economic data. In addition we will highlight the differences in the inventories for the US and Canada compared with the inventories used in the phase 1 of this project. The National Exposure Research Laboratory (NERL) Atmospheric Modeling and Analysis Division (AMAD) conducts research in support of EPA mission to protect human health and the environment. AMAD research program is engaged in developing and evaluating predictive atmospheric models on all spatial and temporal scales for forecasting the air quality and for assessing changes in air quality and air pollutant exposures, as affected by changes in ecosystem management and regulatory decisions. AMAD is responsible for providing a sound scientific and technical basis for regulatory policies based on air quality models to improve ambient air quality. The models developed by AMAD are being used by EPA, NOAA, and the air pollut

Comparison of CMAQ Modeling Study with Discover-AQ 2014 Aircraft Measurements over Colorado

NASA Astrophysics Data System (ADS)

Tang, Y.; Pan, L.; Lee, P.; Tong, D.; Kim, H. C.; Artz, R. S.

2014-12-01

NASA and NCAR jointly led a recent multiple platform-based (space, air and ground) measurement intensive to study air quality and to validate satellite data. The Discover-AQ/FRAPPE field experiment took place along the Colorado Front Range in July and August, 2014. The air quality modeling team of the NOAA Air Resources Laboratory was one of the three teams that provided real-time air quality forecasting for the campaign. The U.S. EPA Community Multi-scale Air Quality (CMAQ) Model was used with emission inventories based on the data set used by the NOAA National Air Quality Forecasting Capacity (NAQFC). By analyzing the forecast results calculated using aircraft measurements, it was found that CO emissions tended to be overestimated, while ethane emissions were underestimated. Biogenic VOCs were also underpredicted. Due to their relatively high altitude, ozone concentrations in Denver and the surrounding areas are affected by both local emissions and transported ozone. The modeled ozone was highly dependent on the meteorological predictions over this region. The complex terrain over the Rocky Mountains also contributed to the model uncertainty. This study discussed the causes of model biases, the forecast performance under different meteorology, and results from using different model grid resolutions. Several data assimilation techniques were further tested to improve the "post-analysis" performance of the modeling system for the period.

Improving short-term air quality predictions over the U.S. using chemical data assimilation

NASA Astrophysics Data System (ADS)

Kumar, R.; Delle Monache, L.; Alessandrini, S.; Saide, P.; Lin, H. C.; Liu, Z.; Pfister, G.; Edwards, D. P.; Baker, B.; Tang, Y.; Lee, P.; Djalalova, I.; Wilczak, J. M.

2017-12-01

State and local air quality forecasters across the United States use air quality forecasts from the National Air Quality Forecasting Capability (NAQFC) at the National Oceanic and Atmospheric Administration (NOAA) as one of the key tools to protect the public from adverse air pollution related health effects by dispensing timely information about air pollution episodes. This project funded by the National Aeronautics and Space Administration (NASA) aims to enhance the decision-making process by improving the accuracy of NAQFC short-term predictions of ground-level particulate matter of less than 2.5 µm in diameter (PM2.5) by exploiting NASA Earth Science Data with chemical data assimilation. The NAQFC is based on the Community Multiscale Air Quality (CMAQ) model. To improve the initialization of PM2.5 in CMAQ, we developed a new capability in the community Gridpoint Statistical Interpolation (GSI) system to assimilate Terra/Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol optical depth (AOD) retrievals in CMAQ. Specifically, we developed new capabilities within GSI to read/write CMAQ data, a forward operator that calculates AOD at 550 nm from CMAQ aerosol chemical composition and an adjoint of the forward operator that translates the changes in AOD to aerosol chemical composition. A generalized background error covariance program called "GEN_BE" has been extended to calculate background error covariance using CMAQ output. The background error variances are generated using a combination of both emissions and meteorological perturbations to better capture sources of uncertainties in PM2.5 simulations. The newly developed CMAQ-GSI system is used to perform daily 24-h PM2.5 forecasts with and without data assimilation from 15 July to 14 August 2014, and the resulting forecasts are compared against AirNOW PM2.5 measurements at 550 stations across the U. S. We find that the assimilation of MODIS AOD retrievals improves initialization of the CMAQ model in terms of improved correlation coefficient and reduced bias. However, we notice a large bias in nighttime PM2.5 simulations which is primarily associated with very shallow boundary layer in the model. The developments and results will be discussed in detail during the presentation.

COMPARISONS OF SPATIAL PATTERNS OF WET DEPOSITION TO MODEL PREDICTIONS

EPA Science Inventory

The Community Multiscale Air Quality model, (CMAQ), is a "one-atmosphere" model, in that it uses a consistent set of chemical reactions and physical principles to predict concentrations of primary pollutants, photochemical smog, and fine aerosols, as well as wet and dry depositi...

Evaluation of emission control strategies to reduce ozone pollution in the Paso del Norte region using a photochemical air quality modeling system

NASA Astrophysics Data System (ADS)

Valenzuela, Victor Hugo

Air pollution emissions control strategies to reduce ozone precursor pollutants are analyzed by applying a photochemical modeling system. Simulations of air quality conditions during an ozone episode which occurred in June, 2006 are undertaken by increasing or reducing area source emissions in Ciudad Juarez, Chihuahua, Mexico. Two air pollutants are primary drivers in the formation of tropospheric ozone. Oxides of nitrogen (NOx) and volatile organic compounds (VOC) undergo multiple chemical reactions under favorable meteorological conditions to form ozone, which is a secondary pollutant that irritates respiratory systems in sensitive individuals especially the elderly and young children. The U.S. Environmental Protection Agency established National Ambient Air Quality Standards (NAAQS) to limit ambient air pollutants such as ozone by establishing an 8-hour average concentration of 0.075 ppm as the threshold at which a violation of the standard occurs. Ozone forms primarily due reactions in the troposphere of NOx and VOC emissions generated primarily by anthropogenic sources in urban regions. Data from emissions inventories indicate area sources account for ˜15 of NOx and ˜45% of regional VOC emissions. Area sources include gasoline stations, automotive paint bodyshops and nonroad mobile sources. Multiplicity of air pollution emissions sources provides an opportunity to investigate and potentially implement air quality improvement strategies to reduce emissions which contribute to elevated ozone concentrations. A baseline modeling scenario was established using the CAMx photochemical air quality model from which a series of sensitivity analyses for evaluating air quality control strategies were conducted. Modifications to area source emissions were made by varying NOx and / or VOC emissions in the areas of particular interest. Model performance was assessed for each sensitivity analysis. Normalized bias (NB) and normalized error (NE) were used to identify variability of the PREDICTED to OBSERVED ozone concentrations of both BASELINE model and simulations with modified emissions assessed by the sensitivity analysis. All simulations were found to vary within acceptable ranges of these two criteria variables. Simulation results indicate ozone formation in the PdN region is VOC-limited. Under VOC-limited conditions, modifications to NOx emissions do not produce a marked increase or decrease in ozone concentrations. Modifications to VOC emissions generated the highest variability in ozone concentrations. Increasing VOC emissions by 75% produced results which minimized model bias and error when comparing PREDICTED and OBSERVED ozone concentrations. Increasing VOC emissions by 75% either alone or in combination with a 75% increase in NOx emissions generated PREDICTED ozone concentrations very near to OBSERVED ozone. By evaluating the changes in ambient ozone concentrations through photochemical modeling, air quality planners may identify the most efficient or effective VOC emissions control strategies for area sources. Among the strategies to achieve emissions reductions are installation of gasoline vapor recovery systems, replacing high-pressure low-volume surface coating paint spray guns with high-volume low-pressure spray paint guns, requiring emissions control booths for surface coating operations as well as undertaking solvent management practices, requiring the sale of low VOC paint solvents in the surface-coating industry, and requiring low-VOC solvents in the dry cleaning industry. Other strategies to reduce VOC emissions include initiating Eco-Driving strategies to reduce fuel consumption from mobile sources and minimize vehicle idling at the international ports of entry by reducing bridge wait times. This dissertation depicts a tool for evaluating impacts of emissions on regional air quality by addressing the highly unresolved fugitive emissions in the Paso del Norte region. It provides a protocol for decision makers to assess the effects of various emission control strategies in the region. Impacts of specific source categories such as the international ports of entry, gasoline stations, paint body shops, truck stops, and military installations on the regional air quality can be easily and systematically addressed in a timely manner in the future.

Meteorological and air pollution modeling for an urban airport

NASA Technical Reports Server (NTRS)

Swan, P. R.; Lee, I. Y.

1980-01-01

Results are presented of numerical experiments modeling meteorology, multiple pollutant sources, and nonlinear photochemical reactions for the case of an airport in a large urban area with complex terrain. A planetary boundary-layer model which predicts the mixing depth and generates wind, moisture, and temperature fields was used; it utilizes only surface and synoptic boundary conditions as input data. A version of the Hecht-Seinfeld-Dodge chemical kinetics model is integrated with a new, rapid numerical technique; both the San Francisco Bay Area Air Quality Management District source inventory and the San Jose Airport aircraft inventory are utilized. The air quality model results are presented in contour plots; the combined results illustrate that the highly nonlinear interactions which are present require that the chemistry and meteorology be considered simultaneously to make a valid assessment of the effects of individual sources on regional air quality.

Prediction of air pollutant concentration based on sparse response back-propagation training feedforward neural networks.

PubMed

Ding, Weifu; Zhang, Jiangshe; Leung, Yee

2016-10-01

In this paper, we predict air pollutant concentration using a feedforward artificial neural network inspired by the mechanism of the human brain as a useful alternative to traditional statistical modeling techniques. The neural network is trained based on sparse response back-propagation in which only a small number of neurons respond to the specified stimulus simultaneously and provide a high convergence rate for the trained network, in addition to low energy consumption and greater generalization. Our method is evaluated on Hong Kong air monitoring station data and corresponding meteorological variables for which five air quality parameters were gathered at four monitoring stations in Hong Kong over 4 years (2012-2015). Our results show that our training method has more advantages in terms of the precision of the prediction, effectiveness, and generalization of traditional linear regression algorithms when compared with a feedforward artificial neural network trained using traditional back-propagation.

Chemical transport model simulations of organic aerosol in ...

EPA Pesticide Factsheets

Gasoline- and diesel-fueled engines are ubiquitous sources of air pollution in urban environments. They emit both primary particulate matter and precursor gases that react to form secondary particulate matter in the atmosphere. In this work, we updated the organic aerosol module and organic emissions inventory of a three-dimensional chemical transport model, the Community Multiscale Air Quality Model (CMAQ), using recent, experimentally derived inputs and parameterizations for mobile sources. The updated model included a revised volatile organic compound (VOC) speciation for mobile sources and secondary organic aerosol (SOA) formation from unspeciated intermediate volatility organic compounds (IVOCs). The updated model was used to simulate air quality in southern California during May and June 2010, when the California Research at the Nexus of Air Quality and Climate Change (CalNex) study was conducted. Compared to the Traditional version of CMAQ, which is commonly used for regulatory applications, the updated model did not significantly alter the predicted organic aerosol (OA) mass concentrations but did substantially improve predictions of OA sources and composition (e.g., POA–SOA split), as well as ambient IVOC concentrations. The updated model, despite substantial differences in emissions and chemistry, performed similar to a recently released research version of CMAQ (Woody et al., 2016) that did not include the updated VOC and IVOC emissions and SOA data

EVALUATING THE PERFORMANCE OF REGIONAL-SCALE PHOTOCHEMICAL MODELING SYSTEMS: PART II--OZONE PREDICTIONS. (R825260)

EPA Science Inventory

In this paper, the concept of scale analysis is applied to evaluate ozone predictions from two regional-scale air quality models. To this end, seasonal time series of observations and predictions from the RAMS3b/UAM-V and MM5/MAQSIP (SMRAQ) modeling systems for ozone were spectra...

Air quality in Delhi during the CommonWealth Games

NASA Astrophysics Data System (ADS)

Marrapu, P.; Cheng, Y.; Beig, G.; Sahu, S.; Srinivas, R.; Carmichael, G. R.

2014-04-01

Air quality during The CommonWealth Games (CWG, held in Delhi in October 2010) is analyzed using a new air quality forecasting system established for the Games. The CWG stimulated enhanced efforts to monitor and model air quality in the region. The air quality of Delhi during the CWG had high levels of particles with mean values of PM2.5 and PM10 at the venues of 111 and 238 μg m-3, respectively. Black carbon (BC) accounted for ∼10% of the PM2.5 mass. It is shown that BC, PM2.5 and PM10 concentrations are well predicted, but with positive biases of ∼25%. The diurnal variations are also well captured, with both the observations and the modeled values showing nighttime maxima and daytime minima. A new emissions inventory, developed as part of this air quality forecasting initiative, is evaluated by comparing the observed and predicted species-species correlations (i.e., BC : CO; BC : PM2.5; PM2.5 : PM10). Assuming that the observations at these sites are representative and that all the model errors are associated with the emissions, then the modeled concentrations and slopes can be made consistent by scaling the emissions by: 0.6 for NOx, 2 for CO, and 0.7 for BC, PM2.5 and PM10. The emission estimates for particles are remarkably good considering the uncertainty in the estimates due to the diverse spread of activities and technologies that take place in Delhi and the rapid rates of change. The contribution of various emission sectors including transportation, power, domestic and industry to surface concentrations are also estimated. Transport, domestic and industrial sectors all make significant contributions to PM levels in Delhi, and the sectoral contributions vary spatially within the city. Ozone levels in Delhi are elevated, with hourly values sometimes exceeding 100 ppb. The continued growth of the transport sector is expected to make ozone pollution a more pressing air pollution problem in Delhi. The sector analysis provides useful inputs into the design of strategies to reduce air pollution levels in Delhi. The contribution for sources outside of Delhi on Delhi air quality range from ∼25% for BC and PM to ∼60% for day time ozone. The significant contributions from non-Delhi sources indicates that in Delhi (as has been show elsewhere) these strategies will also need a more regional perspective.

Chemical transport model simulations of organic aerosol in southern California: model evaluation and gasoline and diesel source contributions

NASA Astrophysics Data System (ADS)

Jathar, Shantanu H.; Woody, Matthew; Pye, Havala O. T.; Baker, Kirk R.; Robinson, Allen L.

2017-03-01

Gasoline- and diesel-fueled engines are ubiquitous sources of air pollution in urban environments. They emit both primary particulate matter and precursor gases that react to form secondary particulate matter in the atmosphere. In this work, we updated the organic aerosol module and organic emissions inventory of a three-dimensional chemical transport model, the Community Multiscale Air Quality Model (CMAQ), using recent, experimentally derived inputs and parameterizations for mobile sources. The updated model included a revised volatile organic compound (VOC) speciation for mobile sources and secondary organic aerosol (SOA) formation from unspeciated intermediate volatility organic compounds (IVOCs). The updated model was used to simulate air quality in southern California during May and June 2010, when the California Research at the Nexus of Air Quality and Climate Change (CalNex) study was conducted. Compared to the Traditional version of CMAQ, which is commonly used for regulatory applications, the updated model did not significantly alter the predicted organic aerosol (OA) mass concentrations but did substantially improve predictions of OA sources and composition (e.g., POA-SOA split), as well as ambient IVOC concentrations. The updated model, despite substantial differences in emissions and chemistry, performed similar to a recently released research version of CMAQ (Woody et al., 2016) that did not include the updated VOC and IVOC emissions and SOA data. Mobile sources were predicted to contribute 30-40 % of the OA in southern California (half of which was SOA), making mobile sources the single largest source contributor to OA in southern California. The remainder of the OA was attributed to non-mobile anthropogenic sources (e.g., cooking, biomass burning) with biogenic sources contributing to less than 5 % to the total OA. Gasoline sources were predicted to contribute about 13 times more OA than diesel sources; this difference was driven by differences in SOA production. Model predictions highlighted the need to better constrain multi-generational oxidation reactions in chemical transport models.

Evaluating ammonia (NH3) predictions in the NOAA National Air Quality Forecast Capability (NAQFC) using in-situ aircraft and satellite measurements from the CalNex2010 campaign

NASA Astrophysics Data System (ADS)

Bray, Casey D.; Battye, William; Aneja, Viney P.; Tong, Daniel; Lee, Pius; Tang, Youhua; Nowak, John B.

2017-08-01

Atmospheric ammonia (NH3) is not only a major precursor gas for fine particulate matter (PM2.5), but it also negatively impacts the environment through eutrophication and acidification. As the need for agriculture, the largest contributing source of NH3, increases, NH3 emissions will also increase. Therefore, it is crucial to accurately predict ammonia concentrations. The objective of this study is to determine how well the U.S. National Oceanic and Atmospheric Administration (NOAA) National Air Quality Forecast Capability (NAQFC) system predicts ammonia concentrations using their Community Multiscale Air Quality (CMAQ) model (v4.6). Model predictions of atmospheric ammonia are compared against measurements taken during the NOAA California Nexus (CalNex) field campaign that took place between May and July of 2010. Additionally, the model predictions were also compared against ammonia measurements obtained from the Tropospheric Emission Spectrometer (TES) on the Aura satellite. The results of this study showed that the CMAQ model tended to under predict concentrations of NH3. When comparing the CMAQ model with the CalNex measurements, the model under predicted NH3 by a factor of 2.4 (NMB = -58%). However, the ratio of the median measured NH3 concentration to the median of the modeled NH3 concentration was 0.8. When compared with the TES measurements, the model under predicted concentrations of NH3 by a factor of 4.5 (NMB = -77%), with a ratio of the median retrieved NH3 concentration to the median of the modeled NH3 concentration of 3.1. Because the model was the least accurate over agricultural regions, it is likely that the major source of error lies within the agricultural emissions in the National Emissions Inventory. In addition to this, the lack of the use of bidirectional exchange of NH3 in the model could also contribute to the observed bias.

Simulating urban-scale air pollutants and their predicting capabilities over the Seoul metropolitan area.

PubMed

Park, Il-Soo; Lee, Suk-Jo; Kim, Cheol-Hee; Yoo, Chul; Lee, Yong-Hee

2004-06-01

Urban-scale air pollutants for sulfur dioxide, nitrogen dioxide, particulate matter with aerodynamic diameter > or = 10 microm, and ozone (O3) were simulated over the Seoul metropolitan area, Korea, during the period of July 2-11, 2002, and their predicting capabilities were discussed. The Air Pollution Model (TAPM) and the highly disaggregated anthropogenic and the biogenic gridded emissions (1 km x 1 km) recently prepared by the Korean Ministry of Environment were applied. Wind fields with observational nudging in the prognostic meteorological model TAPM are optionally adopted to comparatively examine the meteorological impact on the prediction capabilities of urban-scale air pollutants. The result shows that the simulated concentrations of secondary air pollutant largely agree with observed levels with an index of agreement (IOA) of >0.6, whereas IOAs of approximately 0.4 are found for most primary pollutants in the major cities, reflecting the quality of emission data in the urban area. The observationally nudged wind fields with higher IOAs have little effect on the prediction for both primary and secondary air pollutants, implying that the detailed wind field does not consistently improve the urban air pollution model performance if emissions are not well specified. However, the robust highest concentrations are better described toward observations by imposing observational nudging, suggesting the importance of wind fields for the predictions of extreme concentrations such as robust highest concentrations, maximum levels, and >90th percentiles of concentrations for both primary and secondary urban-scale air pollutants.

Building-Scale Atmospheric Modeling for Understanding and Anticipating Environmental Risks to Urban Populations

NASA Astrophysics Data System (ADS)

Warner, T. T.; Swerdlin, S. P.; Chen, F.; Hayden, M.

2009-05-01

The innovative use of Computational Fluid-Dynamics (CFD) models to define the building- and street-scale atmospheric environment in urban areas can benefit society in a number of ways. Design criteria used by architectural climatologists, who help plan the livable cities of the future, require information about air movement within street canyons for different seasons and weather regimes. Understanding indoor urban air- quality problems and their mitigation, especially for older buildings, requires data on air movement and associated dynamic pressures near buildings. Learning how heat waves and anthropogenic forcing in cities collectively affect the health of vulnerable residents is a problem in building thermodynamics, human behavior, and neighborhood-scale and street-canyon-scale atmospheric sciences. And, predicting the movement of plumes of hazardous material released in urban industrial or transportation accidents requires detailed information about vertical and horizontal air motions in the street canyons. These challenges are closer to being addressed because of advances in CFD modeling, the coupling of CFD models with models of indoor air motion and air quality, and the coupling of CFD models with mesoscale weather-prediction models. This paper will review some of the new knowledge and technologies that are being developed to meet these atmospheric-environment needs of our growing urban populations.

Impact of RACM2, halogen chemistry, and updated ozone deposition velocity onhemispheric ozone predictions

EPA Science Inventory

We incorporate the Regional Atmospheric Chemistry Mechanism (RACM2) into the Community Multiscale Air Quality (CMAQ) hemispheric model and compare model predictions to those obtained using the existing Carbon Bond chemical mechanism with the updated toluene chemistry (CB05TU). Th...

Fine Particulate Matter and Cardiovascular Disease: Comparison of Assessment Methods for Long-term Exposure

EPA Science Inventory

Background Adverse cardiovascular events have been linked with PM2.5 exposure obtained primarily from air quality monitors, which rarely co-locate with participant residences. Modeled PM2.5 predictions at finer resolution may more accurately predict residential exposure; however...

Epoxide pathways improve model predictions of isoprene markers and reveal key role of acidity in aerosol formation

EPA Science Inventory

Isoprene significantly contributes to organic aerosol in the southeastern United States where biogenic hydrocarbons mix with anthropogenic emissions. In this work, the Community Multiscale Air Quality model is updated to predict isoprene aerosol from epoxides produced under both ...

Workplace threats to health and job turnover among women workers.

PubMed

Gucer, Patricia W; Oliver, Marc; McDiarmid, Melissa

2003-07-01

Is job turnover related to concern about workplace health risks? Using data from a national sample of working women, we examined the relationships among workplace risk communications, worker concerns about workplace threats from hazardous substances, indoor air quality, and job change. Eight percent reported changing a job as a result of concern over workplace threats to health. Previous workplace injury predicted concern about hazardous materials and indoor air quality as well as job change, but employer communication about workplace health risks was associated with less job change and less concern about indoor air quality. Women worry about workplace threats to their health enough to change their jobs, but employers may have the power to cut turnover costs and reduce disruption to workers' lives through the use of risk communication programs.

Evaluation of MEGAN predicted biogenic isoprene emissions at urban locations in Southeast Texas

NASA Astrophysics Data System (ADS)

Kota, Sri Harsha; Schade, Gunnar; Estes, Mark; Boyer, Doug; Ying, Qi

2015-06-01

Summertime isoprene emissions in the Houston area predicted by the Model of Emissions of Gases and Aerosol from Nature (MEGAN) version 2.1 during the 2006 TexAQS study were evaluated using a source-oriented Community Multiscale Air Quality (CMAQ) Model. Predicted daytime isoprene concentrations at nine surface sites operated by the Texas Commission of Environmental Quality (TCEQ) were significantly higher than local observations when biogenic emissions dominate the total isoprene concentrations, with mean normalized bias (MNB) ranges from 2.0 to 7.7 and mean normalized error (MNE) ranges from 2.2 to 7.7. Predicted upper air isoprene and its first generation oxidation products of methacrolein (MACR) and methyl vinyl ketone (MVK) were also significantly higher (MNB = 8.6, MNE = 9.1) than observations made onboard of NOAA's WP-3 airplane, which flew over the urban area. Over-prediction of isoprene and its oxidation products both at the surface and the upper air strongly suggests that biogenic isoprene emissions in the Houston area are significantly overestimated. Reducing the emission rates by approximately 3/4 was necessary to reduce the error between predictions and observations. Comparison of gridded leaf area index (LAI), plant functional type (PFT) and gridded isoprene emission factor (EF) used in MEGAN modeling with estimates of the same factors from a field survey north of downtown Houston showed that the isoprene over-prediction is likely caused by the combined effects of a large overestimation of the gridded EF in urban Houston and an underestimation of urban LAI. Nevertheless, predicted ozone concentrations in this region were not significantly affected by the isoprene over-predictions, while predicted isoprene SOA and total SOA concentrations can be higher by as much as 50% and 13% using the higher isoprene emission rates, respectively.

Bayesian Maximum Entropy Integration of Ozone Observations and Model Predictions: A National Application.

PubMed

Xu, Yadong; Serre, Marc L; Reyes, Jeanette; Vizuete, William

2016-04-19

To improve ozone exposure estimates for ambient concentrations at a national scale, we introduce our novel Regionalized Air Quality Model Performance (RAMP) approach to integrate chemical transport model (CTM) predictions with the available ozone observations using the Bayesian Maximum Entropy (BME) framework. The framework models the nonlinear and nonhomoscedastic relation between air pollution observations and CTM predictions and for the first time accounts for variability in CTM model performance. A validation analysis using only noncollocated data outside of a validation radius rv was performed and the R(2) between observations and re-estimated values for two daily metrics, the daily maximum 8-h average (DM8A) and the daily 24-h average (D24A) ozone concentrations, were obtained with the OBS scenario using ozone observations only in contrast with the RAMP and a Constant Air Quality Model Performance (CAMP) scenarios. We show that, by accounting for the spatial and temporal variability in model performance, our novel RAMP approach is able to extract more information in terms of R(2) increase percentage, with over 12 times for the DM8A and over 3.5 times for the D24A ozone concentrations, from CTM predictions than the CAMP approach assuming that model performance does not change across space and time.

InMAP: a new model for air pollution interventions

NASA Astrophysics Data System (ADS)

Tessum, C. W.; Hill, J. D.; Marshall, J. D.

2015-10-01

Mechanistic air pollution models are essential tools in air quality management. Widespread use of such models is hindered, however, by the extensive expertise or computational resources needed to run most models. Here, we present InMAP (Intervention Model for Air Pollution), which offers an alternative to comprehensive air quality models for estimating the air pollution health impacts of emission reductions and other potential interventions. InMAP estimates annual-average changes in primary and secondary fine particle (PM2.5) concentrations - the air pollution outcome generally causing the largest monetized health damages - attributable to annual changes in precursor emissions. InMAP leverages pre-processed physical and chemical information from the output of a state-of-the-science chemical transport model (WRF-Chem) within an Eulerian modeling framework, to perform simulations that are several orders of magnitude less computationally intensive than comprehensive model simulations. InMAP uses a variable resolution grid that focuses on human exposures by employing higher spatial resolution in urban areas and lower spatial resolution in rural and remote locations and in the upper atmosphere; and by directly calculating steady-state, annual average concentrations. In comparisons run here, InMAP recreates WRF-Chem predictions of changes in total PM2.5 concentrations with population-weighted mean fractional error (MFE) and bias (MFB) < 10 % and population-weighted R2 ~ 0.99. Among individual PM2.5 species, the best predictive performance is for primary PM2.5 (MFE: 16 %; MFB: 13 %) and the worst predictive performance is for particulate nitrate (MFE: 119 %; MFB: 106 %). Potential uses of InMAP include studying exposure, health, and environmental justice impacts of potential shifts in emissions for annual-average PM2.5. Features planned for future model releases include a larger spatial domain, more temporal information, and the ability to predict ground-level ozone (O3) concentrations. The InMAP model source code and input data are freely available online.

Simulation of air quality impacts from prescribed fires on an urban area.

PubMed

Hu, Yongtao; Odman, M Talat; Chang, Michael E; Jackson, William; Lee, Sangil; Edgerton, Eric S; Baumann, Karsten; Russell, Armistead G

2008-05-15

On February 28, 2007, a severe smoke event caused by prescribed forest fires occurred in Atlanta, GA. Later smoke events in the southeastern metropolitan areas of the United States caused by the Georgia-Florida wild forest fires further magnified the significance of forest fire emissions and the benefits of being able to accurately predict such occurrences. By using preburning information, we utilize an operational forecasting system to simulate the potential air quality impacts from two large February 28th fires. Our "forecast" predicts that the scheduled prescribed fires would have resulted in over 1 million Atlanta residents being potentially exposed to fine particle matter (PM2.5) levels of 35 microg m(-3) or higher from 4 p.m. to midnight. The simulated peak 1 h PM2.5 concentration is about 121 microg m(-3). Our study suggests that the current air quality forecasting technology can be a useful tool for helping the management of fire activities to protect public health. With postburning information, our "hindcast" predictions improved significantly on timing and location and slightly on peak values. "Hindcast" simulations also indicated that additional isoprenoid emissions from pine species temporarily triggered by the fire could induce rapid ozone and secondary organic aerosol formation during late winter. Results from this study suggest that fire induced biogenic volatile organic compounds emissions missing from current fire emissions estimate should be included in the future.

Assessing the Association Between Asthma and Air Quality in the Presence of Wildfires

NASA Astrophysics Data System (ADS)

Young, L. J.; Al-Hamdan, M. Z.; Lopiano, K. K.; Crosson, W. L.; Gotway, C. A.; DuClos, C.; Jordan, M.; Estes, M. G.; Luvall, J. C.; Estes, S. M.; Xu, X.; Holt, N. M.; Leary, E.

2012-12-01

Asthma hospital/emergency room (patient) data are used as the foundation for creating a health outcome indicator of human response to environmental air quality. Daily U.S. Environmental Protection Agency (EPA) Air Quality System (AQS) fine particulates (PM2.5) ground data and the U.S. National Aeronautical Space Administration (NASA) MODIS aerosol optical depth (AOD) data were acquired and processed for years of 2007 and 2008. Figure 1 shows the PM2.5 annual mean composite of all the 2007 B-spline daily surfaces. Initial models for predicting the number of weekly asthma cases within a Florida county has focused on environmental variables. Weekly maximums of PM2.5, relative humidity, and the proportions of the county with smoke and fire were the environmental variables included in the model. Cosine and sine functions of time were used to account for seasonality in asthma cases. Counties were considered to be random effects, thereby adjusting for differences in socio-demographics and other factors. The 2007 predictions for Miami-Dade county when using B-splines PM2.5 are displayed in Figures 2.; PM2.5 annual mean composite of all the 2007 daily surfaces developed using Al-Hamdan et al (2009) B-spline fitting algorithm ; Predicted and observed weekly asthma cases presenting to hospitals or emergency rooms in Miami-Dade county in Florida during 2007

Model simulation of meteorology and air quality during the summer PUMA intensive measurement campaign in the UK West Midlands conurbation.

PubMed

Baggott, Sarah; Cai, Xiaoming; McGregor, Glenn; Harrison, Roy M

2006-05-01

The Regional Atmospheric Modeling System (RAMS) and Urban Airshed Model (UAM IV) have been implemented for prediction of air pollutant concentrations within the West Midlands conurbation of the United Kingdom. The modelling results for wind speed, direction and temperature are in reasonable agreement with observations for two stations, one in a rural area and the other in an urban area. Predictions of surface temperature are generally good for both stations, but the results suggest that the quality of temperature prediction is sensitive to whether cloud cover is reproduced reliably by the model. Wind direction is captured very well by the model, while wind speed is generally overestimated. The air pollution climate of the UK West Midlands is very different to those for which the UAM model was primarily developed, and the methods used to overcome these limitations are described. The model shows a tendency towards under-prediction of primary pollutant (NOx and CO) concentrations, but with suitable attention to boundary conditions and vertical profiles gives fairly good predictions of ozone concentrations. Hourly updating of chemical concentration boundary conditions yields the best results, with input of vertical profiles desirable. The model seriously underpredicts NO2/NO ratios within the urban area and this appears to relate to inadequate production of peroxy radicals. Overall, the chemical reactivity predicted by the model appears to fall well below that occurring in the atmosphere.

Psychosocial and demographic predictors of adherence and non-adherence to health advice accompanying air quality warning systems: a systematic review.

PubMed

D'Antoni, Donatella; Smith, Louise; Auyeung, Vivian; Weinman, John

2017-09-22

Although evidence shows that poor air quality can harm human health, we have a limited understanding about the behavioural impact of air quality forecasts. Our aim was to understand to what extent air quality warning systems influence protective behaviours in the general public, and to identify the demographic and psychosocial factors associated with adherence and non-adherence to the health advice accompanying these warnings. In August 2016 literature was systematically reviewed to find studies assessing intended or actual adherence to health advice accompanying air quality warning systems, and encouraging people to reduce exposure to air pollution. Predictors of adherence to the health advice and/or self-reported reasons for adherence or non-adherence were also systematically reviewed. Studies were included only if they involved participants who were using or were aware of these warning systems. Studies investigating only protective behaviours due to subjective perception of bad air quality alone were excluded. The results were narratively synthesised and discussed within the COM-B theoretical framework. Twenty-one studies were included in the review: seventeen investigated actual adherence; three investigated intended adherence; one assessed both. Actual adherence to the advice to reduce or reschedule outdoor activities during poor air quality episodes ranged from 9.7% to 57% (Median = 31%), whereas adherence to a wider range of protective behaviours (e.g. avoiding busy roads, taking preventative medication) ranged from 17.7% to 98.1% (Median = 46%). Demographic factors did not consistently predict adherence. However, several psychosocial facilitators of adherence were identified. These include knowledge on where to check air quality indices, beliefs that one's symptoms were due to air pollution, perceived severity of air pollution, and receiving advice from health care professionals. Barriers to adherence included: lack of understanding of the indices, being exposed to health messages that reduced both concern about air pollution and perceived susceptibility, as well as perceived lack of self-efficacy/locus of control, reliance on sensory cues and lack of time. We found frequent suboptimal adherence rates to health advice accompanying air quality alerts. Several psychosocial facilitators and barriers of adherence were identified. To maximise their health effects, health advice needs to target these specific psychosocial factors.

Operational prediction of air quality for the United States: applications of satellite observations

NASA Astrophysics Data System (ADS)

Stajner, Ivanka; Lee, Pius; Tong, Daniel; Pan, Li; McQueen, Jeff; Huang, Jianping; Huang, Ho-Chun; Draxler, Roland; Kondragunta, Shobha; Upadhayay, Sikchya

2015-04-01

Operational predictions of ozone and wildfire smoke over United States (U.S.) and predictions of airborne dust over the contiguous 48 states are provided by NOAA at http://airquality.weather.gov/. North American Mesoscale (NAM) weather predictions with inventory based emissions estimates from the U.S. Environmental Protection Agency (EPA) and chemical processes within the Community Multiscale Air Quality (CMAQ) model are combined together to produce ozone predictions. Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model is used to predict wildfire smoke and dust storm predictions. Routine verification of ozone predictions relies on AIRNow compilation of observations from surface monitors. Retrievals of smoke column integrals from GOES satellites and dust column integrals from MODIS satellite instruments are used for verification of smoke and dust predictions. Recent updates of NOAA's operational air quality predictions have focused on mobile emissions using the projections of mobile sources for 2012. Since emission inventories are complex and take years to assemble and evaluate causing a lag of information, we recently began combing inventory information with projections of mobile sources. In order to evaluate this emission update, these changes in projected NOx emissions from 2005-2012 were compared with observed changes in Ozone Monitoring Instrument (OMI) NO2 observations and NOx measured by surface monitors over large U.S. cities over the same period. Comparisons indicate that projected decreases in NOx emissions from 2005 to 2012 are similar, but not as strong as the decreases in the observed NOx concentrations and in OMI NO2 retrievals. Nevertheless, the use of projected mobile NOx emissions in the predictions reduced biases in predicted NOx concentrations, with the largest improvement in the urban areas. Ozone biases are reduced as well, with the largest improvement seen in rural areas. Recent testing of PM2.5 predictions is relying on emissions inventories augmented by real time sources from wildfires and dust storms. The evaluation of these test predictions relies on surface monitor data, but efforts are in progress to include comparisons with satellite observed aerosol optical depth (AOD) products. Testing of PM2.5 predictions continues to exhibit seasonal biases: overprediction in the winter and underprediction in the summer. The current efforts focus on bias correction and development of linkages with global atmospheric composition predictions.

Evaluation of CMAQ and CAMx Ensemble Air Quality Forecasts during the 2015 MAPS-Seoul Field Campaign

NASA Astrophysics Data System (ADS)

Kim, E.; Kim, S.; Bae, C.; Kim, H. C.; Kim, B. U.

2015-12-01

The performance of Air quality forecasts during the 2015 MAPS-Seoul Field Campaign was evaluated. An forecast system has been operated to support the campaign's daily aircraft route decisions for airborne measurements to observe long-range transporting plume. We utilized two real-time ensemble systems based on the Weather Research and Forecasting (WRF)-Sparse Matrix Operator Kernel Emissions (SMOKE)-Comprehensive Air quality Model with extensions (CAMx) modeling framework and WRF-SMOKE- Community Multi_scale Air Quality (CMAQ) framework over northeastern Asia to simulate PM10 concentrations. Global Forecast System (GFS) from National Centers for Environmental Prediction (NCEP) was used to provide meteorological inputs for the forecasts. For an additional set of retrospective simulations, ERA Interim Reanalysis from European Centre for Medium-Range Weather Forecasts (ECMWF) was also utilized to access forecast uncertainties from the meteorological data used. Model Inter-Comparison Study for Asia (MICS-Asia) and National Institute of Environment Research (NIER) Clean Air Policy Support System (CAPSS) emission inventories are used for foreign and domestic emissions, respectively. In the study, we evaluate the CMAQ and CAMx model performance during the campaign by comparing the results to the airborne and surface measurements. Contributions of foreign and domestic emissions are estimated using a brute force method. Analyses on model performance and emissions will be utilized to improve air quality forecasts for the upcoming KORUS-AQ field campaign planned in 2016.

Predicting the Occurrence of Haze Events in Southeast Asia using Machine Learning Algorithms

NASA Astrophysics Data System (ADS)

Lee, H. H.; Chulakadabba, A.; Tonks, A.; Yang, Z.; Wang, C.

2017-12-01

Severe local- and regional-scale air pollution episodes typically originate from 1) high emissions of air pollutants, 2) poor dispersion conditions, and 3) trans-boundary pollutant transport. Biomass burning activities have become more frequent in Southeast Asia, especially in Sumatra, Borneo, and the mainland Southeast. Trans-boundary transport of biomass burning aerosols often lead to air quality problems in the region. Furthermore, particulate pollutants from human activities besides biomass burning also play an important role in the air quality of Southeast Asia. Singapore, for example, has a dynamic industrial sector including chemical, electric and metallurgic industries, and is the region's major petroleum-refining center. In addition, natural gas and oil power plants, waste incinerators, active port traffic, and a major regional airport further complicate Singapore's air quality issues. In this study, we compare five Machine Learning algorithms: k-Nearest Neighbors, Linear Support Vector Machine, Decision Tree, Random Forest and Artificial Neural Network, to identify haze patterns and determine variable importance. The algorithms were trained using local atmospheric data (i.e. months, atmospheric conditions, wind direction and relative humidity) from three observation stations in Singapore (Changi, Seletar and Paya Labar). We find that the algorithms reveal the associations in data within and between the stations, and provide in-depth interpretation of the haze sources. The algorithms also allow us to predict the probability of haze episodes in Singapore and to determine the correlation between this probability and atmospheric conditions.

Investigation of the air pollutant distribution over Northeast Asia using Models-3/CMAQ

NASA Astrophysics Data System (ADS)

Kim, J. Y.; Ghim, Y. S.; Won, J.-G.; Yoon, S.-C.; Woo, J.-H.

2003-04-01

Northeast Asia is one of the most densely populated areas in the world. Huge amount of air pollutants emitted in the area is transported to the east along with prevailing westerlies. In spring of Northeast Asia, migratory anticyclones are frequent. Transport and distribution of air pollutants can be substantially altered according to the locations of anticyclones. In this work, two different synoptic meteorological conditions associated with different locations of anticyclones in May 1999 were identified. The distributions of gaseous and particulate pollutants in these meteorological conditions were predicted and compared. Models-3/CMAQ (USEPA Models-3/Community Multi-scale Air Quality) and MM5 (PSU/NCAR Mesoscale Modeling System) were used to predict air quality and meteorology, respectively. The modeling domain was 5,184 km x 3,456 km centering on the Korean Peninsula (130o N, 40o E). The grid size was 108 km x 108 km and the number of grids was 48 in the west-east direction and 32 in the south-north direction. The number of layers in the vertical direction was six to the height of 500 hPa. Emission data were taken from the Center for Global and Regional Environmental Research, University of Iowa for anthropogenic emissions and from GEIA (Global Emissions Inventory Activity) for biogenic emissions. The GDAPS (Global Data Assimilation and Prediction System) data of six-hour intervals were used for initial and boundary conditions of MM5.

Improve EPA's AIRNow Air Quality Index Maps with NASA/NOAA Satellite Data

NASA Astrophysics Data System (ADS)

Pasch, A.; Zahn, P. H.; DeWinter, J. L.; Haderman, M. D.; White, J. E.; Dickerson, P.; Dye, T. S.; Martin, R. V.

2011-12-01

The U.S. Environmental Protection Agency's (EPA) AIRNow program provides maps of real-time hourly Air Quality Index (AQI) conditions and daily AQI forecasts nationwide (http://www.airnow.gov). The public uses these maps to make decisions concerning their respiratory health. The usefulness of the AIRNow air quality maps depends on the accuracy and spatial coverage of air quality measurements. Currently, the maps use only ground-based measurements, which have significant gaps in coverage in some parts of the United States. As a result, contoured AQI levels have high uncertainty in regions far from monitors. To improve the usefulness of air quality maps, scientists at EPA and Sonoma Technology, Inc. are working in collaboration with the National Aeronautics and Space Administration (NASA), the National Oceanic and Atmospheric Administration (NOAA), and university researchers on a project to incorporate additional measurements into the maps via the AIRNow Satellite Data Processor (ASDP). These measurements include estimated surface PM

Incorporating wind availability into land use regression modelling of air quality in mountainous high-density urban environment.

PubMed

Shi, Yuan; Lau, Kevin Ka-Lun; Ng, Edward

2017-08-01

Urban air quality serves as an important function of the quality of urban life. Land use regression (LUR) modelling of air quality is essential for conducting health impacts assessment but more challenging in mountainous high-density urban scenario due to the complexities of the urban environment. In this study, a total of 21 LUR models are developed for seven kinds of air pollutants (gaseous air pollutants CO, NO 2 , NO x , O 3 , SO 2 and particulate air pollutants PM 2.5 , PM 10 ) with reference to three different time periods (summertime, wintertime and annual average of 5-year long-term hourly monitoring data from local air quality monitoring network) in Hong Kong. Under the mountainous high-density urban scenario, we improved the traditional LUR modelling method by incorporating wind availability information into LUR modelling based on surface geomorphometrical analysis. As a result, 269 independent variables were examined to develop the LUR models by using the "ADDRESS" independent variable selection method and stepwise multiple linear regression (MLR). Cross validation has been performed for each resultant model. The results show that wind-related variables are included in most of the resultant models as statistically significant independent variables. Compared with the traditional method, a maximum increase of 20% was achieved in the prediction performance of annual averaged NO 2 concentration level by incorporating wind-related variables into LUR model development. Copyright © 2017 Elsevier Inc. All rights reserved.

Assessing the influence of land use land cover pattern, socio economic factors and air quality status to predict morbidity on the basis of logistic based regression model

NASA Astrophysics Data System (ADS)

Dixit, A.; Singh, V. K.

2017-12-01

Recent studies conducted by World Health Organisation (WHO) estimated that 92 % of the total world population are living in places where the air quality level has exceeded the WHO standard limit for air quality. This is due to the change in Land Use Land Cover (LULC) pattern, socio economic drivers and anthropogenic heat emission caused by manmade activity. Thereby, many prevalent human respiratory diseases such as lung cancer, chronic obstructive pulmonary disease and emphysema have increased in recent times. In this study, a quantitative relationship is developed between land use (built-up land, water bodies, and vegetation), socio economic drivers and air quality parameters using logistic based regression model over 7 different cities of India for the winter season of 2012 to 2016. Different LULC, socio economic, industrial emission sources, meteorological condition and air quality level from the monitoring stations are taken to estimate the influence on morbidity of each city. Results of correlation are analyzed between land use variables and monthly concentration of pollutants. These values range from 0.63 to 0.76. Similarly, the correlation value between land use variable with socio economic and morbidity ranges from 0.57 to 0.73. The performance of model is improved from 67 % to 79 % in estimating morbidity for the year 2015 and 2016 due to the better availability of observed data.The study highlights the growing importance of incorporating socio-economic drivers with air quality data for evaluating morbidity rate for each city in comparison to just change in quantitative analysis of air quality.

The Impact of Cloud Correction on the Redistribution of Reactive Nitrogen Species

NASA Astrophysics Data System (ADS)

Pour Biazar, A.; McNider, R. T.; Doty, K.; Cameron, R.

2007-12-01

Clouds are particularly important to air quality. Yet, correct prediction of clouds in time and space remains to be a great challenge for the air quality models. One aspect of cloud impact on air quality is the modification of photolysis reaction rates by clouds. Clouds can significantly alter the solar radiation in the wavelengths affecting the photolysis rates. Such modifications significantly impact atmospheric photochemistry and alter the chemical composition of the boundary layer. It also alters the partitioning of chemical compounds by creating a new equilibrium state. Since air quality models are often being used for air quality and emission reduction assessment, understanding the uncertainty caused by inaccurate cloud prediction is imperative. In this study we investigate the radiative impact of clouds in altering the partitioning of nitrogen species in the emission source regions. Such alterations affect the local nitrogen budget and thereby alter the atmospheric composition within the boundary layer. The results from two model simulations, one in which the model predicted clouds are used (control), and the other in which the satellite observed clouds have been assimilated in the model were analyzed. We use satellite retrieved cloud transmissivity, cloud top height, and observed cloud fraction to correct photolysis rates for cloud cover in the Community Multiscale Air Quality (CMAQ) modeling system. The simulations were performed at 4- and 12-km resolution domains over Texas, extending east to Mississippi, for the period of August 24 to August 31, 2000. The results clearly indicate that not using the cloud observations in the model can drastically alter the predicted atmospheric chemical composition within the boundary layer and exaggerate or under-predict the ozone concentrations. Cloud impact is acute and more pronounced over the emission source regions and can lead to drastic errors in the model predictions of ozone and its precursors. Clouds also increased the lifetime of ozone precursors leading to their transport out of the source regions and caused further ozone production downwind. The longer lifetimes for NOx and its transport over regions high in biogenic hydrocarbon emissions (in the eastern part of the domain) led to increased ozone production that was missing in the control simulation. An indirect impact of the clouds in the emission source areas is the alteration in partitioning of nitrogen oxides and the impact on nitrogen budget due to surface removal. This is caused by the disparity between the deposition velocity of NOx and the nitrates that are produced from oxidation of NOx. Under clear skies, NOx undergoes a chemical transformation and produces nitrates such as HNO3 and PAN. In the presence of thick clouds, due to the reduction in the photochemical activities, nitrogen monoxide (NO) rapidly consumes ozone (O3) and produces nitrogen dioxide (NO2) while the production of HNO3 and loss of NOx due to chemical transformation is reduced. Therefore, in one case there is more loss of nitrogen in the vicinity of emission sources. A detailed analysis of two emission source regions, Houston-Galveston and New Orleans area, will be presented. Acknowledgments. This work was accomplished under partial support from Cooperative Agreement between the University of Alabama in Huntsville and the Minerals Management Service on the Gulf of Mexico Issues.

Joint space-time geostatistical model for air quality surveillance

NASA Astrophysics Data System (ADS)

Russo, A.; Soares, A.; Pereira, M. J.

2009-04-01

Air pollution and peoples' generalized concern about air quality are, nowadays, considered to be a global problem. Although the introduction of rigid air pollution regulations has reduced pollution from industry and power stations, the growing number of cars on the road poses a new pollution problem. Considering the characteristics of the atmospheric circulation and also the residence times of certain pollutants in the atmosphere, a generalized and growing interest on air quality issues led to research intensification and publication of several articles with quite different levels of scientific depth. As most natural phenomena, air quality can be seen as a space-time process, where space-time relationships have usually quite different characteristics and levels of uncertainty. As a result, the simultaneous integration of space and time is not an easy task to perform. This problem is overcome by a variety of methodologies. The use of stochastic models and neural networks to characterize space-time dispersion of air quality is becoming a common practice. The main objective of this work is to produce an air quality model which allows forecasting critical concentration episodes of a certain pollutant by means of a hybrid approach, based on the combined use of neural network models and stochastic simulations. A stochastic simulation of the spatial component with a space-time trend model is proposed to characterize critical situations, taking into account data from the past and a space-time trend from the recent past. To identify near future critical episodes, predicted values from neural networks are used at each monitoring station. In this paper, we describe the design of a hybrid forecasting tool for ambient NO2 concentrations in Lisbon, Portugal.

Full-scale chamber investigation and simulation of air freshener emissions in the presence of ozone.

PubMed

Liu, Xiaoyu; Mason, Mark; Krebs, Kenneth; Sparks, Leslie

2004-05-15

Volatile organic compound (VOC) emissions from one electrical plug-in type of pine-scented air freshener and their reactions with O3 were investigated in the U.S. Environmental Protection Agency indoor air research large chamber facility. Ozone was generated from a device marketed as an ozone generator air cleaner. Ozone and oxides of nitrogen concentrations and chamber conditions such as temperature, relative humidity, pressure, and air exchange rate were controlled and/or monitored. VOC emissions and some of the reaction products were identified and quantified. Source emission models were developed to predict the time/concentration profiles of the major VOCs (limonene, alpha-pinene, beta-pinene, 3-carene, camphene, benzyl propionate, benzyl alcohol, bornyl acetate, isobornyl acetate, and benzaldehyde) emitted bythe air freshener. Gas-phase reactions of VOCs from the air freshener with O3 were simulated by a photochemical kinetics simulation system using VOC reaction mechanisms and rate constants adopted from the literature. The concentration-time predictions were in good agreement with the data for O3 and VOCs emitted from the air freshener and with some of the primary reaction products. Systematic differences between the predictions and the experimental results were found for some species. Poor understanding of secondary reactions and heterogeneous chemistry in the chamber is the likely cause of these differences. The method has the potential to provide data to predict the impact of O3/VOC interactions on indoor air quality.

Extreme air pollution events in Hokkaido, Japan, traced back to early snowmelt and large-scale wildfires over East Eurasia: Case studies.

PubMed

Yasunari, Teppei J; Kim, Kyu-Myong; da Silva, Arlindo M; Hayasaki, Masamitsu; Akiyama, Masayuki; Murao, Naoto

2018-04-25

To identify the unusual climate conditions and their connections to air pollutions in a remote area due to wildfires, we examine three anomalous large-scale wildfires in May 2003, April 2008, and July 2014 over East Eurasia, as well as how products of those wildfires reached an urban city, Sapporo, in the northern part of Japan (Hokkaido), significantly affecting the air quality. NASA's MERRA-2 (the Modern-Era Retrospective analysis for Research and Applications, Version 2) aerosol re-analysis data closely reproduced the PM 2.5 variations in Sapporo for the case of smoke arrival in July 2014. Results show that all three cases featured unusually early snowmelt in East Eurasia, accompanied by warmer and drier surface conditions in the months leading to the fires, inducing long-lasting soil dryness and producing climate and environmental conditions conducive to active wildfires. Due to prevailing anomalous synoptic-scale atmospheric motions, smoke from those fires eventually reached a remote area, Hokkaido, and worsened the air quality in Sapporo. In future studies, continuous monitoring of the timing of Eurasian snowmelt and the air quality from the source regions to remote regions, coupled with the analysis of atmospheric and surface conditions, may be essential in more accurately predicting the effects of wildfires on air quality.

Predicting the impacts of new technology aircraft on international air transportation demand

NASA Technical Reports Server (NTRS)

Ausrotas, R. A.

1981-01-01

International air transportation to and from the United States was analyzed. Long term and short term effects and causes of travel are described. The applicability of econometric methods to forecast passenger travel is discussed. A nomograph is developed which shows the interaction of economic growth, airline yields, and quality of service in producing international traffic.

Co-benefits of air quality and climate change policies on air quality of the Mediterranean

NASA Astrophysics Data System (ADS)

Pozzoli, Luca; Mert Gokturk, Ozan; Unal, Alper; Kindap, Tayfun; Janssens-Maenhout, Greet

2015-04-01

The Mediterranean basin is one of the regions of the world where significant impacts due to climate changes are predicted to occur in the future. Observations and model simulations are used to provide to the policy makers scientifically based estimates of the necessity to adjust national emission reductions needed to achieve air quality objectives in the context of a changing climate, which is not only driven by GHGs, but also by short lived climate pollutants, such as tropospheric ozone and aerosols. There is an increasing interest and need to design cost-benefit emission reduction strategies, which could improve both regional air quality and global climate change. In this study we used the WRF-CMAQ air quality modelling system to quantify the contribution of anthropogenic emissions to ozone and particulate matter concentrations in Europe and the Eastern Mediterranean and to understand how this contribution could change in different future scenarios. We have investigated four different future scenarios for year 2050 defined during the European Project CIRCE: a "business as usual" scenario (BAU) where no or just actual measures are taken into account; an "air quality" scenario (BAP) which implements the National Emission Ceiling directive 2001/81/EC member states of the European Union (EU-27); a "climate change" scenario (CC) which implements global climate policies decoupled from air pollution policies; and an "integrated air quality and climate policy" scenario (CAP) which explores the co-benefit of global climate and EU-27 air pollution policies. The BAP scenario largely decreases summer ozone concentrations over almost the entire continent, while the CC and CAP scenarios similarly determine lower decreases in summer ozone but extending all over the Mediterranean, the Middle East countries and Russia. Similar patterns are found for winter PM concentrations; BAP scenario improves pollution levels only in the Western EU countries, and the CAP scenario determines the largest PM reductions over the entire continent and the Mediterranean basin.

Development of an Aura Chemical Reanalysis in support Air Quality Applications

NASA Astrophysics Data System (ADS)

Pierce, R. B.; Lenzen, A.; Schaack, T.

2015-12-01

We present results of chemical data assimilation experiments utilizing the NOAA National Environmental Satellite, Data, and Information Service (NESDIS), University of Wisconsin Space Science and Engineering (SSEC) Real-time Air Quality Modeling System (RAQMS) in conjunction with the NOAA National Centers for Environmental Prediction (NCEP) Operational Gridpoint Statistical Interpolation (GSI) 3-dimensional variational data assimilation system. The impact of assimilating NASA Ozone Monitoring Instrument (OMI) total column ozone, OMI tropospheric nitrogen dioxide columns, and Microwave Limb Sounder (MLS) stratospheric ozone profiles on background ozone is assessed using measurements from the 2010 NSF High-performance Instrumented Airborne Platform for Environmental Research (HIAPER) Pole-to-Pole Observation (HIPPO) and NOAA California Nexus (CalNex) campaigns. Results show that the RAQMS/GSI Chemical Reanalysis is able to provide very good estimates of background ozone and large-scale ozone variability and is suitable for use in constraining regional air quality modeling activities. These experiments are being used to guide the development of a multi-year global chemical and aerosol reanalysis using NASA Aura and A-Train measurements to support air quality applications.

Using NASA Satellite Aerosol Optical Depth to Enhance PM2.5 Concentration Datasets for Use in Human Health and Epidemiology Studies

NASA Astrophysics Data System (ADS)

Huff, A. K.; Weber, S.; Braggio, J.; Talbot, T.; Hall, E.

2012-12-01

Fine particulate matter (PM2.5) is a criterion air pollutant, and its adverse impacts on human health are well established. Traditionally, studies that analyze the health effects of human exposure to PM2.5 use concentration measurements from ground-based monitors and predicted PM2.5 concentrations from air quality models, such as the U.S. EPA's Community Multi-scale Air Quality (CMAQ) model. There are shortcomings associated with these datasets, however. Monitors are not distributed uniformly across the U.S., which causes spatially inhomogeneous measurements of pollutant concentrations. There are often temporal variations as well, since not all monitors make daily measurements. Air quality model output, while spatially and temporally uniform, represents predictions of PM2.5 concentrations, not actual measurements. This study is exploring the potential of combining Aerosol Optical Depth (AOD) data from the MODIS instrument on NASA's Terra and Aqua satellites with PM2.5 monitor data and CMAQ predictions to create PM2.5 datasets that more accurately reflect the spatial and temporal variations in ambient PM2.5 concentrations on the metropolitan scale, with the overall goal of enhancing capabilities for environmental public health decision-making. AOD data provide regional information about particulate concentrations that can fill in the spatial and temporal gaps in the national PM2.5 monitor network. Furthermore, AOD is a measurement, so it reflects actual concentrations of particulates in the atmosphere, in contrast to PM2.5 predictions from air quality models. Results will be presented from the Battelle/U.S. EPA statistical Hierarchical Bayesian Model (HBM), which was used to combine three PM2.5 concentration datasets: monitor measurements, AOD data, and CMAQ model predictions. The study is focusing on the Baltimore, MD and New York City, NY metropolitan regions for the period 2004-2006. For each region, combined monitor/AOD/CMAQ PM2.5 datasets generated by the HBM are being correlated with data on inpatient hospitalizations and emergency room visits for seven respiratory and cardiovascular diseases using statistical case-crossover analyses. Preliminary results will be discussed regarding the potential for the addition of AOD data to increase the correlation between PM2.5 concentrations and health outcomes. Environmental public health tracking programs associated with the Maryland Department of Health and Mental Hygiene, the New York State Department of Health, the CDC, and the U.S. EPA have expressed interest in using the results of this study to enhance their existing environmental health surveillance activities.

Influence of trans-boundary air pollution from China on multi-day high PM10 episodes in Seoul, Korea

NASA Astrophysics Data System (ADS)

Oh, H. R.; Ho, C. H.; Kim, J.; Chen, D.; Lee, S.; Choi, Y. S.; Chang, L. S.; Song, C. K.

2014-12-01

Air quality problems have become a serious global issue as it causes over 3 million deaths per year all over the world. With generations of massive air pollutants in China, the effects of trans-boundary transports of air pollutants on human health have become a serious international concern in East Asia. However, only a limited number of studies are available for providing scientific evidences for quantifying the sources and transports of air pollutants over major countries in East Asia. Here, it is shown that particulate matters originated from China played major role in the occurrence of multi-day (≥ 4 days) severe air pollution episodes in Seoul, Korea, in which the concentration of particulate matter of diameters ≤ 10 μm exceeds 100 μg m-3. Observations show that these multi-day severe air quality episodes occur when a strong high-pressure system resides over the eastern China - Korea region. Such a weather condition confines air pollutants within the atmospheric boundary layer and spread them by slow westerlies within the boundary layer from China into the neighboring countries. Understanding such dynamical processes is a key for advancing the predictability of trans-boundary air pollutants and their health impacts in East Asia as well as developing international measures to improve air quality for the region.

Visualizing and communicating uncertainty in the earth and environmental sciences: a review

NASA Astrophysics Data System (ADS)

Pebesma, Edzer

2014-05-01

I will review past attempts to visualising uncertainty in spatial or spatio-temporal predictions of groundwater quality, quality predictions, sea bed sediment, bird densities, air quality measurements, and exposure to air quality of individuals and populations. The attempts involved software development (aguila [1], greenland [2]), the development of standards for communicating uncertain spatial and spatio-temporal information (UncertML, [3]), and have been illustrated by applications in a number of EU projects (Apmosphere [4], INTAMAP [5], UncertWeb [6] and GeoViQua [7]). I will also report on usability studies that were carried out (e.g. [8]). [1] http://pcraster.geo.uu.nl/projects/developments/aguila/ [2] https://wiki.52north.org/bin/view/Geostatistics/Greenland [3] http://www.uncertml.org/ [4] http://www.apmosphere.org/ [5] http://www.intamap.org/ [6] http://www.uncertweb.org/ [7] http://www.geoviqua.org/ [8] Senaratne, H. L. Gerharz, E. Pebesma, A. Schwering, 2012. Usability of Spatio-Temporal Uncertainty Visualisation Methods. In: Bridging the Geographic Information Sciences, Lecture Notes in Geoinformation and Cartography, J. Gensel, D. Josselin and D. Vandenbroucke. Springer Berlin Heidelberg.

Evaluation of a seven-year air quality simulation using the Weather Research and Forecasting (WRF)/Community Multiscale Air Quality (CMAQ) models in the eastern United States.

PubMed

Zhang, Hongliang; Chen, Gang; Hu, Jianlin; Chen, Shu-Hua; Wiedinmyer, Christine; Kleeman, Michael; Ying, Qi

2014-03-01

The performance of the Weather Research and Forecasting (WRF)/Community Multi-scale Air Quality (CMAQ) system in the eastern United States is analyzed based on results from a seven-year modeling study with a 4-km spatial resolution. For 2-m temperature, the monthly averaged mean bias (MB) and gross error (GE) values are generally within the recommended performance criteria, although temperature is over-predicted with MB values up to 2K. Water vapor at 2-m is well-predicted but significant biases (>2 g kg(-1)) were observed in wintertime. Predictions for wind speed are satisfactory but biased towards over-prediction with 0

“Transference Ratios” to Predict Total Oxidized Sulfur and Nitrogen Deposition – Part II, Modeling Results

EPA Science Inventory

The current study examines predictions of transference ratios and related modeled parameters for oxidized sulfur and oxidized nitrogen using five years (2002-2006) of 12-km grid cell-specific annual estimates from EPA’s Community Air Quality Model (CMAQ) for five selected sub-re...

EVALUATING THE PERFORMANCE OF REGIONAL-SCALE PHOTOCHEMICAL MODELING SYSTEMS: PART I--METEOROLOGICAL PREDICTIONS. (R825260)

EPA Science Inventory

In this study, the concept of scale analysis is applied to evaluate two state-of-science meteorological models, namely MM5 and RAMS3b, currently being used to drive regional-scale air quality models. To this end, seasonal time series of observations and predictions for temperatur...

EFFECTS OF USING THE CB05 VERSUS THE CB4 CHEMICAL MECHANISMS ON MODEL PREDICTIONS

EPA Science Inventory

The Carbon Bond 4 (CB4) chemical mechanism has been widely used for many years in box and air quality models to predict the effect of atmospheric chemistry on pollutant concentrations. Because of the importance of this mechanism and the length of time since its original developm...

EFFECTS OF USING THE CB05 VERSUS THE CB4 CHEMICAL MECHANISM ON MODEL PREDICTIONS

EPA Science Inventory

The Carbon Bond 4 (CB4) chemical mechanism has been widely used for many years in box and air quality models to predict the effect of atmospheric chemistry on pollutant concentrations. Because of the importance of this mechanism and the length of time since its original developm...

“Transference Ratios” to Predict Total Oxidized Sulfur and Nitrogen Deposition – Part I, Monitoring Results

EPA Science Inventory

Use of model-predicted “transference ratios” is currently under consideration by the US EPA in the formulation of a Secondary National Ambient Air Quality Standard for oxidized nitrogen and oxidized sulfur. This term is an empirical parameter defined for oxidized sulfur (TS)as th...

The Covariance between Air Pollution Annoyance and Noise Annoyance, and Its Relationship with Health-Related Quality of Life.

PubMed

Shepherd, Daniel; Dirks, Kim; Welch, David; McBride, David; Landon, Jason

2016-08-06

Air pollution originating from road traffic is a known risk factor of respiratory and cardiovascular disease (both in terms of chronic and acute effects). While adverse effects on cardiovascular health have also been linked with noise (after controlling for air pollution), noise exposure has been commonly linked to sleep impairment and negative emotional reactions. Health is multi-faceted, both conceptually and operationally; Health-Related Quality of Life (HRQOL) is one of many measures capable of probing health. In this study, we examine pre-collected data from postal surveys probing HRQOL obtained from a variety of urban, suburban, and rural contexts across the North Island of New Zealand. Analyses focus on the covariance between air pollution annoyance and noise annoyances, and their independent and combined effects on HRQOL. Results indicate that the highest ratings of air pollution annoyance and noise annoyances were for residents living close to the motorway, while the lowest were for rural residents. Most of the city samples indicated no significant difference between air pollution- and noise-annoyance ratings, and of all of the correlations between air pollution- and noise-annoyance, the highest were found in the city samples. These findings suggest that annoyance is driven by exposure to environmental factors and not personality characteristics. Analysis of HRQOL indicated that air pollution annoyance predicts greater variability in the physical HRQOL domain while noise annoyance predicts greater variability in the psychological, social and environmental domains. The lack of an interaction effect between air pollution annoyance and noise annoyance suggests that air pollution and noise impact on health independently. These results echo those obtained from objective measures of health and suggest that mitigation of traffic effects should address both air and noise pollution.

The Covariance between Air Pollution Annoyance and Noise Annoyance, and Its Relationship with Health-Related Quality of Life

PubMed Central

Shepherd, Daniel; Dirks, Kim; Welch, David; McBride, David; Landon, Jason

2016-01-01

Air pollution originating from road traffic is a known risk factor of respiratory and cardiovascular disease (both in terms of chronic and acute effects). While adverse effects on cardiovascular health have also been linked with noise (after controlling for air pollution), noise exposure has been commonly linked to sleep impairment and negative emotional reactions. Health is multi-faceted, both conceptually and operationally; Health-Related Quality of Life (HRQOL) is one of many measures capable of probing health. In this study, we examine pre-collected data from postal surveys probing HRQOL obtained from a variety of urban, suburban, and rural contexts across the North Island of New Zealand. Analyses focus on the covariance between air pollution annoyance and noise annoyances, and their independent and combined effects on HRQOL. Results indicate that the highest ratings of air pollution annoyance and noise annoyances were for residents living close to the motorway, while the lowest were for rural residents. Most of the city samples indicated no significant difference between air pollution- and noise-annoyance ratings, and of all of the correlations between air pollution- and noise-annoyance, the highest were found in the city samples. These findings suggest that annoyance is driven by exposure to environmental factors and not personality characteristics. Analysis of HRQOL indicated that air pollution annoyance predicts greater variability in the physical HRQOL domain while noise annoyance predicts greater variability in the psychological, social and environmental domains. The lack of an interaction effect between air pollution annoyance and noise annoyance suggests that air pollution and noise impact on health independently. These results echo those obtained from objective measures of health and suggest that mitigation of traffic effects should address both air and noise pollution. PMID:27509512

Climate and human intervention effects on future fire activity and consequences for air pollution across the 21st century

NASA Astrophysics Data System (ADS)

Val Martin, M.; Pierce, J. R.; Heald, C. L.; Li, F.; Lawrence, D. M.; Wiedinmyer, C.; Tilmes, S.; Vitt, F.

2016-12-01

Emissions of aerosols and gases from fires have been shown to adversely affect air quality across the world. Fire activity is strongly related to climate and anthropogenic activities. Current fire projections for the 21st century seem very uncertain, ranging from increasing to declining depending on the climate, land cover change and population growth scenarios used. Here we present an analysis of the changes in future wildfire activity and consequences on air quality, with focus on PM2.5 and surface O3 over regions vulnerable to fire. We use the global Community Earth System Model (CESM) with a process-based fire model to simulate emissions from agriculture, peatland, deforestation and landscape fires for present-day and throughout the current century. We consider two future Representative Concentration Pathways climate scenarios combined with population density changes predicted from Shared Socio-economic Pathways to project climate and demographic effects on fire activity and further consequences for future air quality.

Numerical prediction on the dispersion of pollutant particles

NASA Astrophysics Data System (ADS)

Osman, Kahar; Ali, Zairi; Ubaidullah, S.; Zahid, M. N.

2012-06-01

The increasing concern on air pollution has led people around the world to find more efficient ways to control the problem. Air dispersion modeling is proven to be one of the alternatives that provide economical ways to control the growing threat of air pollution. The objective of this research is to develop a practical numerical algorithm to predict the dispersion of pollutant particles around a specific source of emission. The source selected was a rubber wood manufacturing plant. Gaussian-plume model were used as air dispersion model due to its simplicity and generic application. Results of this study show the concentrations of the pollutant particles on ground level reached approximately 90μg/m3, compared with other software. This value surpasses the limit of 50μg/m3 stipulated by the National Ambient Air Quality Standard (NAAQS) and Recommended Malaysian Guidelines (RMG) set by Environment Department of Malaysia. The results also show high concentration of pollutant particles reading during dru seasons as compared to that of rainy seasons. In general, the developed algorithm is proven to be able to predict particles distribution around emitted source with acceptable accuracy.

Improved Impact of Atmospheric Infrared Sounder (AIRS) Radiance Assimilation in Numerical Weather Prediction

NASA Technical Reports Server (NTRS)

Zavodsky, Bradley; Chou, Shih-Hung; Jedlovec, Gary

2012-01-01

Improvements to global and regional numerical weather prediction (NWP) have been demonstrated through assimilation of data from NASA s Atmospheric Infrared Sounder (AIRS). Current operational data assimilation systems use AIRS radiances, but impact on regional forecasts has been much smaller than for global forecasts. Retrieved profiles from AIRS contain much of the information that is contained in the radiances and may be able to reveal reasons for this reduced impact. Assimilating AIRS retrieved profiles in an identical analysis configuration to the radiances, tracking the quantity and quality of the assimilated data in each technique, and examining analysis increments and forecast impact from each data type can yield clues as to the reasons for the reduced impact. By doing this with regional scale models individual synoptic features (and the impact of AIRS on these features) can be more easily tracked. This project examines the assimilation of hyperspectral sounder data used in operational numerical weather prediction by comparing operational techniques used for AIRS radiances and research techniques used for AIRS retrieved profiles. Parallel versions of a configuration of the Weather Research and Forecasting (WRF) model with Gridpoint Statistical Interpolation (GSI) that mimics the analysis methodology, domain, and observational datasets for the regional North American Mesoscale (NAM) model run at the National Centers for Environmental Prediction (NCEP)/Environmental Modeling Center (EMC) are run to examine the impact of each type of AIRS data set. The first configuration will assimilate the AIRS radiance data along with other conventional and satellite data using techniques implemented within the operational system; the second configuration will assimilate AIRS retrieved profiles instead of AIRS radiances in the same manner. Preliminary results of this study will be presented and focus on the analysis impact of the radiances and profiles for selected cases.

Using proxies to explore ensemble uncertainty in climate impact studies: the example of air pollution

NASA Astrophysics Data System (ADS)

Lemaire, V. E. P.; Colette, A.; Menut, L.

2015-10-01

Because of its sensitivity to unfavorable weather patterns, air pollution is sensitive to climate change so that, in the future, a climate penalty could jeopardize the expected efficiency of air pollution mitigation measures. A common method to assess the impact of climate on air quality consists in implementing chemistry-transport models forced by climate projection. However, the computing cost of such method requires optimizing ensemble exploration techniques. By using a training dataset of deterministic projection of climate and air quality over Europe, we identified the main meteorological drivers of air quality for 8 regions in Europe and developed simple statistical models that could be used to predict air pollutant concentrations. The evolution of the key climate variables driving either particulate or gaseous pollution allows concluding on the robustness of the climate impact on air quality. The climate benefit for PM2.5 was confirmed -0.96 (±0.18), -1.00 (±0.37), -1.16 ± (0.23) μg m-3, for resp. Eastern Europe, Mid Europe and Northern Italy and for the Eastern Europe, France, Iberian Peninsula, Mid Europe and Northern Italy regions a climate penalty on ozone was identified 10.11 (±3.22), 8.23 (±2.06), 9.23 (±1.13), 6.41 (±2.14), 7.43 (±2.02) μg m-3. This technique also allows selecting a subset of relevant regional climate model members that should be used in priority for future deterministic projections.

Spatial and temporal air quality pattern recognition using environmetric techniques: a case study in Malaysia.

PubMed

Syed Abdul Mutalib, Sharifah Norsukhairin; Juahir, Hafizan; Azid, Azman; Mohd Sharif, Sharifah; Latif, Mohd Talib; Aris, Ahmad Zaharin; Zain, Sharifuddin M; Dominick, Doreena

2013-09-01

The objective of this study is to identify spatial and temporal patterns in the air quality at three selected Malaysian air monitoring stations based on an eleven-year database (January 2000-December 2010). Four statistical methods, Discriminant Analysis (DA), Hierarchical Agglomerative Cluster Analysis (HACA), Principal Component Analysis (PCA) and Artificial Neural Networks (ANNs), were selected to analyze the datasets of five air quality parameters, namely: SO2, NO2, O3, CO and particulate matter with a diameter size of below 10 μm (PM10). The three selected air monitoring stations share the characteristic of being located in highly urbanized areas and are surrounded by a number of industries. The DA results show that spatial characterizations allow successful discrimination between the three stations, while HACA shows the temporal pattern from the monthly and yearly factor analysis which correlates with severe haze episodes that have happened in this country at certain periods of time. The PCA results show that the major source of air pollution is mostly due to the combustion of fossil fuel in motor vehicles and industrial activities. The spatial pattern recognition (S-ANN) results show a better prediction performance in discriminating between the regions, with an excellent percentage of correct classification compared to DA. This study presents the necessity and usefulness of environmetric techniques for the interpretation of large datasets aiming to obtain better information about air quality patterns based on spatial and temporal characterizations at the selected air monitoring stations.

Performance evaluation of air quality models for predicting PM10 and PM2.5 concentrations at urban traffic intersection during winter period.

PubMed

Gokhale, Sharad; Raokhande, Namita

2008-05-01

There are several models that can be used to evaluate roadside air quality. The comparison of the operational performance of different models pertinent to local conditions is desirable so that the model that performs best can be identified. Three air quality models, namely the 'modified General Finite Line Source Model' (M-GFLSM) of particulates, the 'California Line Source' (CALINE3) model, and the 'California Line Source for Queuing & Hot Spot Calculations' (CAL3QHC) model have been identified for evaluating the air quality at one of the busiest traffic intersections in the city of Guwahati. These models have been evaluated statistically with the vehicle-derived airborne particulate mass emissions in two sizes, i.e. PM10 and PM2.5, the prevailing meteorology and the temporal distribution of the measured daily average PM10 and PM2.5 concentrations in wintertime. The study has shown that the CAL3QHC model would make better predictions compared to other models for varied meteorology and traffic conditions. The detailed study reveals that the agreements between the measured and the modeled PM10 and PM2.5 concentrations have been reasonably good for CALINE3 and CAL3QHC models. Further detailed analysis shows that the CAL3QHC model performed well compared to the CALINE3. The monthly performance measures have also led to the similar results. These two models have also outperformed for a class of wind speed velocities except for low winds (<1 m s(-1)), for which, the M-GFLSM model has shown the tendency of better performance for PM10. Nevertheless, the CAL3QHC model has outperformed for both the particulate sizes and for all the wind classes, which therefore can be optional for air quality assessment at urban traffic intersections.

Asthma and Air Quality in the Presence of Fires - A Foundation for Public Health Policy in Florida

NASA Technical Reports Server (NTRS)

Crosson, William; Al-Hamdan, Mohammad; Estes, Maurice, Jr.; Estes, Sue; Luvall, Jeffrey; Sifford, Cody; Young, Linda

2012-01-01

Outdoor air quality and its associated impacts on respiratory problems in Florida are of public health significance. Air quality in Florida can be poor during the extended wildfire season, threatening persons with compromised respiratory systems each year. Studies have demonstrated that particulate matter, which is generally elevated in the vicinity of wildfires, is associated with increases in hospital admissions and occurrences of acute asthma exacerbations. However, few studies have examined the modifying effect of socio-demographic characteristics of cities or regional areas on the relationship between air quality and health outcomes. In an ongoing university/multi-agency project, asthma hospital/emergency room (patient) data are being used to create a health outcome indicator of human response to environmental air quality. Environmental data are derived from satellite measurements, with special attention being given to the effect of wildfires and prescribed burns on air quality. This presentation will focus on the environmental data sets particulate matter, location of fires, smoke plumes that are being collected and processed for linkage with health data. After this linkage has been performed, space-time models of asthma rates as a function of air quality data and socio-demographic variables will be developed and validated. The Florida Department of Health (FDOH) will work with county health department staff and representatives from the medical community to establish a protocol with triggers for issuing public health advisories/alerts based on the developed and validated health outcome indicators. From this effort, a science-based policy for issuing public health advisories/alerts for asthma relating to air quality will be developed, giving FDOH the ability to (1) predict, with stated levels of uncertainty, case load of hospital admissions based on air quality, (2) reduce asthma exacerbations by forewarning asthmatics to limit outside activities on poor air quality days, (3) apply management practices on the rates of hospital/emergency room visits for asthma, and (4) provide information that would help translate interventions into policy decisions, thereby reducing the economic burden and increasing well being of asthmatics. Further, the results of the study will be incorporated into Florida s Environmental Public Health Tracking (EPHT) program, which is part of the Centers for Disease Control and Prevention's (CDC's) EPHT network.

Evaluation of Observation-Fused Regional Air Quality Model Results for Population Air Pollution Exposure Estimation

PubMed Central

Chen, Gang; Li, Jingyi; Ying, Qi; Sherman, Seth; Perkins, Neil; Rajeshwari, Sundaram; Mendola, Pauline

2014-01-01

In this study, Community Multiscale Air Quality (CMAQ) model was applied to predict ambient gaseous and particulate concentrations during 2001 to 2010 in 15 hospital referral regions (HRRs) using a 36-km horizontal resolution domain. An inverse distance weighting based method was applied to produce exposure estimates based on observation-fused regional pollutant concentration fields using the differences between observations and predictions at grid cells where air quality monitors were located. Although the raw CMAQ model is capable of producing satisfying results for O3 and PM2.5 based on EPA guidelines, using the observation data fusing technique to correct CMAQ predictions leads to significant improvement of model performance for all gaseous and particulate pollutants. Regional average concentrations were calculated using five different methods: 1) inverse distance weighting of observation data alone, 2) raw CMAQ results, 3) observation-fused CMAQ results, 4) population-averaged raw CMAQ results and 5) population-averaged fused CMAQ results. It shows that while O3 (as well as NOx) monitoring networks in the HRR regions are dense enough to provide consistent regional average exposure estimation based on monitoring data alone, PM2.5 observation sites (as well as monitors for CO, SO2, PM10 and PM2.5 components) are usually sparse and the difference between the average concentrations estimated by the inverse distance interpolated observations, raw CMAQ and fused CMAQ results can be significantly different. Population-weighted average should be used to account spatial variation in pollutant concentration and population density. Using raw CMAQ results or observations alone might lead to significant biases in health outcome analyses. PMID:24747248

Updating sea spray aerosol emissions in the Community Multiscale Air Quality (CMAQ) model version 5.0.2

NASA Astrophysics Data System (ADS)

Gantt, B.; Kelly, J. T.; Bash, J. O.

2015-11-01

Sea spray aerosols (SSAs) impact the particle mass concentration and gas-particle partitioning in coastal environments, with implications for human and ecosystem health. Model evaluations of SSA emissions have mainly focused on the global scale, but regional-scale evaluations are also important due to the localized impact of SSAs on atmospheric chemistry near the coast. In this study, SSA emissions in the Community Multiscale Air Quality (CMAQ) model were updated to enhance the fine-mode size distribution, include sea surface temperature (SST) dependency, and reduce surf-enhanced emissions. Predictions from the updated CMAQ model and those of the previous release version, CMAQv5.0.2, were evaluated using several coastal and national observational data sets in the continental US. The updated emissions generally reduced model underestimates of sodium, chloride, and nitrate surface concentrations for coastal sites in the Bay Regional Atmospheric Chemistry Experiment (BRACE) near Tampa, Florida. Including SST dependency to the SSA emission parameterization led to increased sodium concentrations in the southeastern US and decreased concentrations along parts of the Pacific coast and northeastern US. The influence of sodium on the gas-particle partitioning of nitrate resulted in higher nitrate particle concentrations in many coastal urban areas due to increased condensation of nitric acid in the updated simulations, potentially affecting the predicted nitrogen deposition in sensitive ecosystems. Application of the updated SSA emissions to the California Research at the Nexus of Air Quality and Climate Change (CalNex) study period resulted in a modest improvement in the predicted surface concentration of sodium and nitrate at several central and southern California coastal sites. This update of SSA emissions enabled a more realistic simulation of the atmospheric chemistry in coastal environments where marine air mixes with urban pollution.

Merging Air Quality and Public Health Decision Support Systems

NASA Astrophysics Data System (ADS)

Hudspeth, W. B.; Bales, C. L.

2003-12-01

The New Mexico Air Quality Mapper (NMAQM) is a Web-based, open source GIS prototype application that Earth Data Analysis Center is developing under a NASA Cooperative Agreement. NMAQM enhances and extends existing data and imagery delivery systems with an existing Public Health system called the Rapid Syndrome Validation Project (RSVP). RSVP is a decision support system operating in several medical and public health arenas. It is evolving to ingest remote sensing data as input to provide early warning of human health threats, especially those related to anthropogenic atmospheric pollutants and airborne pathogens. The NMAQM project applies measurements of these atmospheric pollutants, derived from both remotely sensed data as well as from in-situ air quality networks, to both forecasting and retrospective analyses that influence human respiratory health. NMAQM provides a user-friendly interface for visualizing and interpreting environmentally-linked epidemiological phenomena. The results, and the systems made to provide the information, will be applicable not only to decision-makers in the public health realm, but also to air quality organizations, demographers, community planners, and other professionals in information technology, and social and engineering sciences. As an accessible and interactive mapping and analysis application, it allows environment and health personnel to study historic data for hypothesis generation and trend analysis, and then, potentially, to predict air quality conditions from daily data acquisitions. Additional spin off benefits to such users include the identification of gaps in the distribution of in-situ monitoring stations, the dissemination of air quality data to the public, and the discrimination of local vs. more regional sources of air pollutants that may bear on decisions relating to public health and public policy.

Air pollution interventions and their impact on public health.

PubMed

Henschel, Susann; Atkinson, Richard; Zeka, Ariana; Le Tertre, Alain; Analitis, Antonis; Katsouyanni, Klea; Chanel, Olivier; Pascal, Mathilde; Forsberg, Bertil; Medina, Sylvia; Goodman, Patrick G

2012-10-01

Numerous epidemiological studies have found a link between air pollution and health. We are reviewing a collection of published intervention studies with particular focus on studies assessing both improvements in air quality and associated health effects. Interventions, defined as events aimed at reducing air pollution or where reductions occurred as a side effect, e.g. strikes, German reunification, from the 1960s onwards were considered for inclusion. This review is not a complete record of all existing air pollution interventions. In total, 28 studies published in English were selected based on a systematic search of internet databases. Overall air pollution interventions have succeeded at improving air quality. Consistently published evidence suggests that most of these interventions have been associated with health benefits, mainly by the way of reduced cardiovascular and/or respiratory mortality and/or morbidity. The decrease in mortality from the majority of the reviewed interventions has been estimated to exceed the expected predicted figures based on the estimates from time-series studies. There is consistent evidence that decreased air pollution levels following an intervention resulted in health benefits for the assessed population.

Linking Asthma Exacerbation and Air Pollution Data: A Step Toward Public Health and Environmental Data Integration

NASA Technical Reports Server (NTRS)

Faruque, Fazlay; Finley, Richard; Marshall, Gailen; Brackin, Bruce; Li, Hui; Williams, Worth; Al-Hamdan, Mohammad; Luvall, Jeffrey; Rickman, Doug; Crosson, Bill

2006-01-01

Studies have shown that reducing exposure to triggers such as air pollutants can reduce symptoms and the need for medication in asthma patients. However, systems that track asthma are generally not integrated with those that track environmental hazards related to asthma. Tlvs lack of integration hinders public health awareness and responsiveness to these environmental triggers. The current study is a collaboration between health and environmental professionals to utilize NASA-derived environmental data to develop a decision support system (DSS) for asthma prediction, surveillance, and intervention. The investigators link asthma morbidity data from the University of Mississippi Medical Center (UMMC) and Mississippi Department of Health (MDH) with air quality data from the Mississippi Department of Environmental Quality (MDEQ) and remote sensing data from NASA. Daily ambient environmental hazard data for PM2.5 and ozone are obtained from the MDEQ air quality monitoring locations and are combined with remotely sensed data from NASA to develop a state-wide spatial and time series profile of environmental air quality. These data are then used to study the correlation of these measures of air quality variation with the asthma exacerbation incidence throughout the state over time. The goal is to utilize these readily available measures to allow real-time risk assessment for asthma exacerbations. GeoMedStat, a DSS previously developed for biosurveillance, will integrate these measures to monitor, analyze and report the real-time risk assessment for asthma exacerbation throughout the state.

Modeling the Dynamic Change of Air Quality and its Response to Emission Trends

NASA Astrophysics Data System (ADS)

Zhou, Wei

This thesis focuses on evaluating atmospheric chemistry and transport models' capability in simulating the chemistry and dynamics of power plant plumes, evaluating their strengths and weaknesses in predicting air quality trends at regional scales, and exploring air quality trends in an urban area. First, the Community Mutlti-scale Air Quality (CMAQ) model is applied to simulate the physical and chemical evolution of power plant plumes (PPPs) during the second Texas Air Quality Study (TexAQS) in 2006. SO2 and NOy were observed to be rapidly removed from PPPs on cloudy days but not on cloud-free days, indicating efficient aqueous processing of these compounds in clouds, while the model fails to capture the rapid loss of SO2 and NOy in some plumes on the cloudy day. Adjustments to cloud liquid water content (QC) and the default metal concentrations in the cloud module could explain some of the SO 2 loss while NOy in the model was insensitive to QC. Second, CMAQ is applied to simulate the ozone (O3) change after the NO x SIP Call and mobile emission controls in the eastern U.S. from 2002 to 2006. Observed downward changes in 8-hour O3 concentrations in the NOx SIP Call region were under-predicted by 26%--66%. The under-prediction in O3 improvements could be alleviated by 5%--31% by constraining NOx emissions in each year based on observed NOx concentrations while temperature biases or uncertainties in chemical reactions had minor impact on simulated O3 trends. Third, changes in ozone production in the Houston area is assessed with airborne measurements from TexAQS 2000 and 2006. Simultaneous declines in nitrogen oxides (NOx=NO+NO2) and highly reactive Volatile Organic Compounds (HRVOCs) were observed in the Houston Ship Channel (HSC). The reduction in HRVOCs led to the decline in total radical concentration by 20-50%. Rapid ozone production rates in the Houston area declined by 40-50% from 2000 to 2006, to which the reduction in NOx and HRVOCs had the similar contribution. Houston petrochemical and urban plumes largely remained in a strong VOC-sensitive regime of ozone formation and maintained high Ozone Production Efficiency (OPE: 5-15).

Long short-term memory neural network for air pollutant concentration predictions: Method development and evaluation.

PubMed

Li, Xiang; Peng, Ling; Yao, Xiaojing; Cui, Shaolong; Hu, Yuan; You, Chengzeng; Chi, Tianhe

2017-12-01

Air pollutant concentration forecasting is an effective method of protecting public health by providing an early warning against harmful air pollutants. However, existing methods of air pollutant concentration prediction fail to effectively model long-term dependencies, and most neglect spatial correlations. In this paper, a novel long short-term memory neural network extended (LSTME) model that inherently considers spatiotemporal correlations is proposed for air pollutant concentration prediction. Long short-term memory (LSTM) layers were used to automatically extract inherent useful features from historical air pollutant data, and auxiliary data, including meteorological data and time stamp data, were merged into the proposed model to enhance the performance. Hourly PM 2.5 (particulate matter with an aerodynamic diameter less than or equal to 2.5 μm) concentration data collected at 12 air quality monitoring stations in Beijing City from Jan/01/2014 to May/28/2016 were used to validate the effectiveness of the proposed LSTME model. Experiments were performed using the spatiotemporal deep learning (STDL) model, the time delay neural network (TDNN) model, the autoregressive moving average (ARMA) model, the support vector regression (SVR) model, and the traditional LSTM NN model, and a comparison of the results demonstrated that the LSTME model is superior to the other statistics-based models. Additionally, the use of auxiliary data improved model performance. For the one-hour prediction tasks, the proposed model performed well and exhibited a mean absolute percentage error (MAPE) of 11.93%. In addition, we conducted multiscale predictions over different time spans and achieved satisfactory performance, even for 13-24 h prediction tasks (MAPE = 31.47%). Copyright © 2017 Elsevier Ltd. All rights reserved.

Speed and Delay Prediction Models for Planning Applications

DOT National Transportation Integrated Search

1999-01-01

Estimation of vehicle speed and delay is fundamental to many forms of : transportation planning analyses including air quality, long-range travel : forecasting, major investment studies, and congestion management systems. : However, existing planning...

A database and tool for boundary conditions for regional air quality modeling: description and evaluation

NASA Astrophysics Data System (ADS)

Henderson, B. H.; Akhtar, F.; Pye, H. O. T.; Napelenok, S. L.; Hutzell, W. T.

2014-02-01

Transported air pollutants receive increasing attention as regulations tighten and global concentrations increase. The need to represent international transport in regional air quality assessments requires improved representation of boundary concentrations. Currently available observations are too sparse vertically to provide boundary information, particularly for ozone precursors, but global simulations can be used to generate spatially and temporally varying lateral boundary conditions (LBC). This study presents a public database of global simulations designed and evaluated for use as LBC for air quality models (AQMs). The database covers the contiguous United States (CONUS) for the years 2001-2010 and contains hourly varying concentrations of ozone, aerosols, and their precursors. The database is complemented by a tool for configuring the global results as inputs to regional scale models (e.g., Community Multiscale Air Quality or Comprehensive Air quality Model with extensions). This study also presents an example application based on the CONUS domain, which is evaluated against satellite retrieved ozone and carbon monoxide vertical profiles. The results show performance is largely within uncertainty estimates for ozone from the Ozone Monitoring Instrument and carbon monoxide from the Measurements Of Pollution In The Troposphere (MOPITT), but there were some notable biases compared with Tropospheric Emission Spectrometer (TES) ozone. Compared with TES, our ozone predictions are high-biased in the upper troposphere, particularly in the south during January. This publication documents the global simulation database, the tool for conversion to LBC, and the evaluation of concentrations on the boundaries. This documentation is intended to support applications that require representation of long-range transport of air pollutants.

Effect of Climate Change on Water Temperature and ...

EPA Pesticide Factsheets

There is increasing evidence that our planet is warming and this warming is also resulting in rising sea levels. Estuaries which are located at the interface between land and ocean are impacted by these changes. We used CE-QUAL-W2 water quality model to predict changes in water temperature as a function of increasing air temperatures and rising sea level for the Yaquina Estuary, Oregon (USA). Annual average air temperature in the Yaquina watershed is expected to increase about 0.3 deg C per decade by 2040-2069. An air temperature increase of 3 deg C in the Yaquina watershed is likely to result in estuarine water temperature increasing by 0.7 to 1.6 deg C. Largest water temperature increases are expected in the upper portion of the estuary, while sea level rise may ameliorate some of the warming in the lower portion of the estuary. Smallest changes in water temperature are predicted to occur in the summer, and maximum changes during the winter and spring. Increases in air temperature may result in an increase in the number of days per year that the 7-day maximum average temperature exceeds 18 deg C (criterion for protection of rearing and migration of salmonids and trout) as well as other water quality concerns. In the upstream portion of the estuary, a 4 deg C increase in air temperature is predicted to cause an increase of 40 days not meeting the temperature criterion, while in the lower estuary the increase will depend upon rate of sea level rise (rang

Multi-year downscaling application of two-way coupled WRF v3.4 and CMAQ v5.0.2 over east Asia for regional climate and air quality modeling: model evaluation and aerosol direct effects

NASA Astrophysics Data System (ADS)

Hong, Chaopeng; Zhang, Qiang; Zhang, Yang; Tang, Youhua; Tong, Daniel; He, Kebin

2017-06-01

In this study, a regional coupled climate-chemistry modeling system using the dynamical downscaling technique was established by linking the global Community Earth System Model (CESM) and the regional two-way coupled Weather Research and Forecasting - Community Multi-scale Air Quality (WRF-CMAQ) model for the purpose of comprehensive assessments of regional climate change and air quality and their interactions within one modeling framework. The modeling system was applied over east Asia for a multi-year climatological application during 2006-2010, driven with CESM downscaling data under Representative Concentration Pathways 4.5 (RCP4.5), along with a short-term air quality application in representative months in 2013 that was driven with a reanalysis dataset. A comprehensive model evaluation was conducted against observations from surface networks and satellite observations to assess the model's performance. This study presents the first application and evaluation of the two-way coupled WRF-CMAQ model for climatological simulations using the dynamical downscaling technique. The model was able to satisfactorily predict major meteorological variables. The improved statistical performance for the 2 m temperature (T2) in this study (with a mean bias of -0.6 °C) compared with the Coupled Model Intercomparison Project Phase 5 (CMIP5) multi-models might be related to the use of the regional model WRF and the bias-correction technique applied for CESM downscaling. The model showed good ability to predict PM2. 5 in winter (with a normalized mean bias (NMB) of 6.4 % in 2013) and O3 in summer (with an NMB of 18.2 % in 2013) in terms of statistical performance and spatial distributions. Compared with global models that tend to underpredict PM2. 5 concentrations in China, WRF-CMAQ was able to capture the high PM2. 5 concentrations in urban areas. In general, the two-way coupled WRF-CMAQ model performed well for both climatological and air quality applications. The coupled modeling system with direct aerosol feedbacks predicted aerosol optical depth relatively well and significantly reduced the overprediction in downward shortwave radiation at the surface (SWDOWN) over polluted regions in China. The performance of cloud variables was not as good as other meteorological variables, and underpredictions of cloud fraction resulted in overpredictions of SWDOWN and underpredictions of shortwave and longwave cloud forcing. The importance of climate-chemistry interactions was demonstrated via the impacts of aerosol direct effects on climate and air quality. The aerosol effects on climate and air quality in east Asia (e.g., SWDOWN and T2 decreased by 21.8 W m-2 and 0.45 °C, respectively, and most pollutant concentrations increased by 4.8-9.5 % in January over China's major cities) were more significant than in other regions because of higher aerosol loadings that resulted from severe regional pollution, which indicates the need for applying online-coupled models over east Asia for regional climate and air quality modeling and to study the important climate-chemistry interactions. This work established a baseline for WRF-CMAQ simulations for a future period under the RCP4.5 climate scenario, which will be presented in a future paper.

Modeling the Impacts of Global Climate and Regional Land Use Change on Regional Climate, Air Quality and Public Health in the New York Metropolitan Region

NASA Astrophysics Data System (ADS)

Rosenthal, J. E.; Knowlton, K. M.; Kinney, P. L.

2002-12-01

There is an imminent need to downscale the global climate models used by international consortiums like the IPCC (Intergovernmental Panel on Climate Change) to predict the future regional impacts of climate change. To meet this need, a "place-based" climate model that makes specific regional projections about future environmental conditions local inhabitants could face is being created by the Mailman School of Public Health at Columbia University, in collaboration with other researchers and universities, for New York City and the 31 surrounding counties. This presentation describes the design and initial results of this modeling study, aimed at simulating the effects of global climate change and regional land use change on climate and air quality over the northeastern United States in order to project the associated public health impacts in the region. Heat waves and elevated concentrations of ozone and fine particles are significant current public health stressors in the New York metropolitan area. The New York Climate and Health Project is linking human dimension and natural sciences models to assess the potential for future public health impacts from heat stress and air quality, and yield improved tools for assessing climate change impacts. The model will be applied to the NY metropolitan east coast region. The following questions will be addressed: 1. What changes in the frequency and severity of extreme heat events are likely to occur over the next 80 years due to a range of possible scenarios of land use and land cover (LU/LC) and climate change in the region? 2. How might the frequency and severity of episodic concentrations of ozone (O3) and airborne particulate matter smaller than 2.5 æm in diameter (PM2.5) change over the next 80 years due to a range of possible scenarios of land use and climate change in the metropolitan region? 3. What is the range of possible human health impacts of these changes in the region? 4. How might projected future human exposures and responses to heat stress and air quality differ as a function of socio-economic status and race/ethnicity across the region? The model systems used for this study are the Goddard Institute for Space Studies (GISS) Global Atmosphere-Ocean Model; the Regional Atmospheric Modeling System (RAMS) and PennState/NCAR MM5 mesoscale meteorological models; the SLEUTH land use model; the Sparse Matrix Operator Kernel Emissions Modeling System (SMOKE); the Community Multiscale Air Quality (CMAQ) and Comprehensive Air Quality Model with Extensions (CAMx) models for simulating regional air quality; and exposure-risk coefficients for assessing population health impacts based on exposure to extreme heat, fine particulates (PM2.5) and ozone. Two different IPCC global emission scenarios and two different regional land use growth scenarios are considered in the simulations, spanning a range of possible futures. In addition to base simulations for selected time periods in the decade 1990 - 2000, the integrated model is used to simulate future scenarios in the 2020s, 2050s, and 2080s. Predictions from both the meteorological models and the air quality models are compared against available observations for the simulations in the 1990s to establish baseline model performance. A series of sensitivity tests will address whether changes in meteorology due to global climate change, changes in regional land use, or changes in emissions have the largest impact on predicted ozone and particulate matter concentrations.

Machine learning and hurdle models for improving regional predictions of stream water acid neutralizing capacity

Treesearch

Nicholas A. Povak; Paul F. Hessburg; Keith M. Reynolds; Timothy J. Sullivan; Todd C. McDonnell; R. Brion Salter

2013-01-01

In many industrialized regions of the world, atmospherically deposited sulfur derived from industrial, nonpoint air pollution sources reduces stream water quality and results in acidic conditions that threaten aquatic resources. Accurate maps of predicted stream water acidity are an essential aid to managers who must identify acid-sensitive streams, potentially...

Urban local air quality management framework for non-attainment areas in Indian cities.

PubMed

Gulia, Sunil; Nagendra, S M Shiva; Barnes, Jo; Khare, Mukesh

2018-04-01

Increasing urban air pollution level in Indian cities is one of the major concerns for policy makers due to its impact on public health. The growth in population and increase in associated motorised road transport demand is one of the major causes of increasing air pollution in most urban areas along with other sources e.g., road dust, construction dust, biomass burning etc. The present study documents the development of an urban local air quality management (ULAQM) framework at urban hotspots (non-attainment area) and a pathway for the flow of information from goal setting to policy making. The ULAQM also includes assessment and management of air pollution episodic conditions at these hotspots, which currently available city/regional-scale air quality management plans do not address. The prediction of extreme pollutant concentrations using a hybrid model differentiates the ULAQM from other existing air quality management plans. The developed ULAQM framework has been applied and validated at one of the busiest traffic intersections in Delhi and Chennai cities. Various scenarios have been tested targeting the effective reductions in elevated levels of NO x and PM 2.5 concentrations. The results indicate that a developed ULAQM framework is capable of providing an evidence-based graded action to reduce ambient pollution levels within the specified standard level at pre-identified locations. The ULAQM framework methodology is generalised and therefore can be applied to other non-attainment areas of the country. Copyright © 2017 Elsevier B.V. All rights reserved.

Wildland Fire Research: Tools and Technology Development

EPA Pesticide Factsheets

Scientific tools are needed to better quantify and predict the impact of smoke from wildlfires on public health. EPA research is supporting the development of new air quality monitors, advancing modeling and improving emissions inventories.

Exploring the potential relationship between indoor air quality and the concentration of airborne culturable fungi: a combined experimental and neural network modeling study.

PubMed

Liu, Zhijian; Cheng, Kewei; Li, Hao; Cao, Guoqing; Wu, Di; Shi, Yunjie

2018-02-01

Indoor airborne culturable fungi exposure has been closely linked to occupants' health. However, conventional measurement of indoor airborne fungal concentration is complicated and usually requires around one week for fungi incubation in laboratory. To provide an ultra-fast solution, here, for the first time, a knowledge-based machine learning model is developed with the inputs of indoor air quality data for estimating the concentration of indoor airborne culturable fungi. To construct a database for statistical analysis and model training, 249 data groups of air quality indicators (concentration of indoor airborne culturable fungi, indoor/outdoor PM 2.5 and PM 10 concentrations, indoor temperature, indoor relative humidity, and indoor CO 2 concentration) were measured from 85 residential buildings of Baoding (China) during the period of 2016.11.15-2017.03.15. Our results show that artificial neural network (ANN) with one hidden layer has good prediction performances, compared to a support vector machine (SVM). With the tolerance of ± 30%, the prediction accuracy of the ANN model with ten hidden nodes can at highest reach 83.33% in the testing set. Most importantly, we here provide a quick method for estimating the concentration of indoor airborne fungi that can be applied to real-time evaluation.

Global Environmental Multiscale model - a platform for integrated environmental predictions

NASA Astrophysics Data System (ADS)

Kaminski, Jacek W.; Struzewska, Joanna; Neary, Lori; Dearden, Frank

2017-04-01

The Global Environmental Multiscale model was developed by the Government of Canada as an operational weather prediction model in the mid-1990s. Subsequently, it was used as the host meteorological model for an on-line implementation of air quality chemistry and aerosols from global to the meso-gamma scale. Further model developments led to the vertical extension of the modelling domain to include stratospheric chemistry, aerosols, and formation of polar stratospheric clouds. In parallel, the modelling platform was used for planetary applications where dynamical, radiative transfer and chemical processes in the atmosphere of Mars were successfully simulated. Undoubtedly, the developed modelling platform can be classified as an example capable of the seamless and coupled modelling of the dynamics and chemistry of planetary atmospheres. We will present modelling results for global, regional, and local air quality episodes and the long-term air quality trends. Upper troposphere and lower stratosphere modelling results will be presented in terms of climate change and subsonic aviation emissions modelling. Model results for the atmosphere of Mars will be presented in the context of the 2016 ExoMars mission and the anticipated observations from the NOMAD instrument. Also, we will present plans and the design to extend the GEM model to the F region with further coupling with a magnetospheric model that extends to 15 Re.

Forecasting asthma-related hospital admissions in London using negative binomial models.

PubMed

Soyiri, Ireneous N; Reidpath, Daniel D; Sarran, Christophe

2013-05-01

Health forecasting can improve health service provision and individual patient outcomes. Environmental factors are known to impact chronic respiratory conditions such as asthma, but little is known about the extent to which these factors can be used for forecasting. Using weather, air quality and hospital asthma admissions, in London (2005-2006), two related negative binomial models were developed and compared with a naive seasonal model. In the first approach, predictive forecasting models were fitted with 7-day averages of each potential predictor, and then a subsequent multivariable model is constructed. In the second strategy, an exhaustive search of the best fitting models between possible combinations of lags (0-14 days) of all the environmental effects on asthma admission was conducted. Three models were considered: a base model (seasonal effects), contrasted with a 7-day average model and a selected lags model (weather and air quality effects). Season is the best predictor of asthma admissions. The 7-day average and seasonal models were trivial to implement. The selected lags model was computationally intensive, but of no real value over much more easily implemented models. Seasonal factors can predict daily hospital asthma admissions in London, and there is a little evidence that additional weather and air quality information would add to forecast accuracy.

Investigation of the impact of higher molecular weight organics on OH reactivity in London

NASA Astrophysics Data System (ADS)

Holmes, Rachel; Hamilton, Jacqueline; Hopkins, Jimmy; Lee, James; Lidster, Richard; Lewis, Alistair

2014-05-01

Volatile organic compounds (VOCs) play an important role in the formation of pollution in the air, particularly in the boundary layer of the atmosphere. VOCs in an urban atmosphere react with radical species to form ozone (O3), which at ground levels can pose a significant threat to health.[1] Air quality models have been developed to predict the effect of emissions on air quality. Numerous studies of urban environments show discrepancies between measured and predicted estimates of the lifetime of OH radicals. One possibility is that the magnitude of VOCs as a sink for reactive species is underestimated in models, including unmeasured and larger aromatic species. To study some of these additional compounds we have developed a method using comprehensive two dimensional gas chromatography coupled to a flame ionisation detector (GC×GC-FID). GC×GC is a hyphenated technique where two columns are coupled together via a modulator, providing two discrete separations of each species based on boiling point and polarity.[2] This provides a high resolution method, with increased separation power and improved peak capacity when compared to many single column systems.[3] This technique was used in conjunction with a dual channel GC (DC-GC) during the Clean Air for London (ClearfLo) project to increase the speciation of the complex air matrix. Target compounds were in the range C1 to C13+ VOCs, including oxygenates, aromatics, saturated and unsaturated aliphatics. Calculations of the pseudo first order OH reactivity indicates that higher carbon number VOCs may account for some of the missing OH sinks in comparison to emission inventory estimates. During summer measurements the role of biogenic VOCs increases, with isoprene and monoterpenes acting as important OH sinks. Including these should enhance the prediction capability of air quality models. This can then lead to the introduction of new policies for the reduction of pollution precursors and hopefully result in improved air quality. References 1. Atkinson, R., Atmospheric chemistry of VOCs and NOx. Atmospheric Environment, 2000. 34(12-14): p. 2063-2101. 2. Hamilton, J.F., Using Comprehensive Two-Dimensional Gas Chromatography to Study the Atmosphere. Journal of Chromatographic Science, 2010. 48(4): p. 274-. 3. Lidster, R.T., J.F. Hamilton, and A.C. Lewis, The application of two total transfer valve modulators for comprehensive two-dimensional gas chromatography of volatile organic compounds. Journal of Separation Science, 2011. 34(7): p. 812-821.

Evaluation of model-predicted hazardous air pollutants (HAPs) near a mid-sized U.S. airport

NASA Astrophysics Data System (ADS)

Vennam, Lakshmi Pradeepa; Vizuete, William; Arunachalam, Saravanan

2015-10-01

Accurate modeling of aircraft-emitted pollutants in the vicinity of airports is essential to study the impact on local air quality and to answer policy and health-impact related issues. To quantify air quality impacts of airport-related hazardous air pollutants (HAPs), we carried out a fine-scale (4 × 4 km horizontal resolution) Community Multiscale Air Quality model (CMAQ) model simulation at the T.F. Green airport in Providence (PVD), Rhode Island. We considered temporally and spatially resolved aircraft emissions from the new Aviation Environmental Design Tool (AEDT). These model predictions were then evaluated with observations from a field campaign focused on assessing HAPs near the PVD airport. The annual normalized mean error (NME) was in the range of 36-70% normalized mean error for all HAPs except for acrolein (>70%). The addition of highly resolved aircraft emissions showed only marginally incremental improvements in performance (1-2% decrease in NME) of some HAPs (formaldehyde, xylene). When compared to a coarser 36 × 36 km grid resolution, the 4 × 4 km grid resolution did improve performance by up to 5-20% NME for formaldehyde and acetaldehyde. The change in power setting (from traditional International Civil Aviation Organization (ICAO) 7% to observation studies based 4%) doubled the aircraft idling emissions of HAPs, but led to only a 2% decrease in NME. Overall modeled aircraft-attributable contributions are in the range of 0.5-28% near a mid-sized airport grid-cell with maximum impacts seen only within 4-16 km from the airport grid-cell. Comparison of CMAQ predictions with HAP estimates from EPA's National Air Toxics Assessment (NATA) did show similar annual mean concentrations and equally poor performance. Current estimates of HAPs for PVD are a challenge for modeling systems and refinements in our ability to simulate aircraft emissions have made only incremental improvements. Even with unrealistic increases in HAPs aviation emissions the model could not match observed concentrations near the runway airport site. Our results suggest other uncertainties in the modeling system such as meteorology, HAPs chemistry, or other emission sources require increased scrutiny.

A Coupled Surface Nudging Scheme for use in Retrospective ...

EPA Pesticide Factsheets

A surface analysis nudging scheme coupling atmospheric and land surface thermodynamic parameters has been implemented into WRF v3.8 (latest version) for use with retrospective weather and climate simulations, as well as for applications in air quality, hydrology, and ecosystem modeling. This scheme is known as the flux-adjusting surface data assimilation system (FASDAS) developed by Alapaty et al. (2008). This scheme provides continuous adjustments for soil moisture and temperature (via indirect nudging) and for surface air temperature and water vapor mixing ratio (via direct nudging). The simultaneous application of indirect and direct nudging maintains greater consistency between the soil temperature–moisture and the atmospheric surface layer mass-field variables. The new method, FASDAS, consistently improved the accuracy of the model simulations at weather prediction scales for different horizontal grid resolutions, as well as for high resolution regional climate predictions. This new capability has been released in WRF Version 3.8 as option grid_sfdda = 2. This new capability increased the accuracy of atmospheric inputs for use air quality, hydrology, and ecosystem modeling research to improve the accuracy of respective end-point research outcome. IMPACT: A new method, FASDAS, was implemented into the WRF model to consistently improve the accuracy of the model simulations at weather prediction scales for different horizontal grid resolutions, as wel

Environmental Impact of Megacities - Results from CityZen

NASA Astrophysics Data System (ADS)

Gauss, M.

2012-04-01

Megacities have increasingly important impacts on air quality and climate change on different spatial scales, owing to their high population densities and concentrated emission sources. The EU FP7 project CityZen (Megacity - Zoom for the Environment) ended in 2011 and was, together with its sister project MEGAPOLI, part of a major research effort within FP7 on megacities in Europe and worldwide. The project mainly focused on air pollution trends in large cities and emission hotspots, climate-chemistry couplings, future projections, and emission mitigation options. Both observational and modeling tools have been extensively used. This paper reviews some of the main results from CityZen regarding present air pollution in and around megacities, future scenarios and mitigation options to reduce air pollution and/or climate change, and the main policy messages from the project. The different observed trends over European and Asian hotspots during the last 10 to 15 years are shown. Results of source attribution of pollutants, which have been measured and calculated in and around the different selected hot spots in CityZen will be discussed. Another important question to be addressed is the extent to which climate change will affect air quality and the effectiveness of air quality legislation. Although projected emission reductions are a major determinate influencing the predictions of future air pollution, model results suggest that climate change has to be taken into account when devising future air quality legislation. This paper will also summarize some important policy messages in terms of ozone, particles and the observational needs that have been put forward as conclusions from the project.

Evaluation of a two-dimensional numerical model for air quality simulation in a street canyon

NASA Astrophysics Data System (ADS)

Okamoto, Shin `Ichi; Lin, Fu Chi; Yamada, Hiroaki; Shiozawa, Kiyoshige

For many urban areas, the most severe air pollution caused by automobile emissions appears along a road surrounded by tall buildings: the so=called street canyon. A practical two-dimensional numerical model has been developed to be applied to this kind of road structure. This model contains two submodels: a wind-field model and a diffusion model based on a Monte Carlo particle scheme. In order to evaluate the predictive performance of this model, an air quality simulation was carried out at three trunk roads in the Tokyo metropolitan area: Nishi-Shimbashi, Aoyama and Kanda-Nishikicho (using SF 6 as a tracer and NO x measurement). Since this model has two-dimensional properties and cannot be used for the parallel wind condition, the perpendicular wind condition was selected for the simulation. The correlation coefficients for the SF 6 and NO x data in Aoyama were 0.67 and 0.62, respectively. When predictive performance of this model is compared with other models, this model is comparable to the SRI model, and superior to the APPS three-dimensional numerical model.

Forecasting daily source air quality using multivariate statistical analysis and radial basis function networks.

PubMed

Sun, Gang; Hoff, Steven J; Zelle, Brian C; Nelson, Minda A

2008-12-01

It is vital to forecast gas and particle matter concentrations and emission rates (GPCER) from livestock production facilities to assess the impact of airborne pollutants on human health, ecological environment, and global warming. Modeling source air quality is a complex process because of abundant nonlinear interactions between GPCER and other factors. The objective of this study was to introduce statistical methods and radial basis function (RBF) neural network to predict daily source air quality in Iowa swine deep-pit finishing buildings. The results show that four variables (outdoor and indoor temperature, animal units, and ventilation rates) were identified as relative important model inputs using statistical methods. It can be further demonstrated that only two factors, the environment factor and the animal factor, were capable of explaining more than 94% of the total variability after performing principal component analysis. The introduction of fewer uncorrelated variables to the neural network would result in the reduction of the model structure complexity, minimize computation cost, and eliminate model overfitting problems. The obtained results of RBF network prediction were in good agreement with the actual measurements, with values of the correlation coefficient between 0.741 and 0.995 and very low values of systemic performance indexes for all the models. The good results indicated the RBF network could be trained to model these highly nonlinear relationships. Thus, the RBF neural network technology combined with multivariate statistical methods is a promising tool for air pollutant emissions modeling.

Application of data mining to the analysis of meteorological data for air quality prediction: A case study in Shenyang

NASA Astrophysics Data System (ADS)

Zhao, Chang; Song, Guojun

2017-08-01

Air pollution is one of the important reasons for restricting the current economic development. PM2.5 which is a vital factor in the measurement of air pollution is defined as a kind of suspended particulate matter with its equivalent diameter less than 25μm, which may enter the alveoli and therefore make a great impact on the human body. Meteorological factors are also one of the main factors affecting the production of PM2.5, therefore, it is essential to establish the model between meteorological factors and PM2.5 for the prediction. Data mining is a promising approach to model PM2.5 change, Shenyang which is one of the most important industrial city in Northeast China with severe air pollutions is set as the case city. Meteorological data (wind direction, wind speed, temperature, humidity, rainfall, etc.) from 2013 to 2015 and PM2.5 concentration data are used for this prediction. As to the requirements of the World Health Organization (WHO), three data mining models, whereby the predictions of PM2.5 are directly generated by the meteorological data. After assessment, the random forest model is appeared to offer better prediction performance than the other two. At last, the accuracy of the generated models are analysed.

Improvement in fresh fruit and vegetable logistics quality: berry logistics field studies.

PubMed

do Nascimento Nunes, M Cecilia; Nicometo, Mike; Emond, Jean Pierre; Melis, Ricardo Badia; Uysal, Ismail

2014-06-13

Shelf life of fresh fruits and vegetables is greatly influenced by environmental conditions. Increasing temperature usually results in accelerated loss of quality and shelf-life reduction, which is not physically visible until too late in the supply chain to adjust logistics to match shelf life. A blackberry study showed that temperatures inside pallets varied significantly and 57% of the berries arriving at the packinghouse did not have enough remaining shelf life for the longest supply routes. Yet, the advanced shelf-life loss was not physically visible. Some of those pallets would be sent on longer supply routes than necessary, creating avoidable waste. Other studies showed that variable pre-cooling at the centre of pallets resulted in physically invisible uneven shelf life. We have shown that using simple temperature measurements much waste can be avoided using 'first expiring first out'. Results from our studies showed that shelf-life prediction should not be based on a single quality factor as, depending on the temperature history, the quality attribute that limits shelf life may vary. Finally, methods to use air temperature to predict product temperature for highest shelf-life prediction accuracy in the absence of individual sensors for each monitored product have been developed. Our results show a significant reduction of up to 98% in the root-mean-square-error difference between the product temperature and air temperature when advanced estimation methods are used.

Evaluation of the AirNow Satellite Data Processor for 2010-2012

NASA Astrophysics Data System (ADS)

Pasch, A. N.; DeWinter, J. L.; Dye, T.; Haderman, M.; Zahn, P. H.; Szykman, J.; White, J. E.; Dickerson, P.; van Donkelaar, A.; Martin, R.

2013-12-01

The U.S. Environmental Protection Agency's (EPA) AirNow program provides the public with real-time and forecasted air quality conditions. Millions of people each day use information from AirNow to protect their health. The AirNow program (http://www.airnow.gov) reports ground-level ozone (O3) and fine particulate matter (PM2.5) with a standardized index called the Air Quality Index (AQI). AirNow aggregates information from over 130 state, local, and federal air quality agencies and provides tools for over 2,000 agency staff responsible for monitoring, forecasting, and communicating local air quality. Each hour, AirNow systems generate thousands of maps and products. The usefulness of the AirNow air quality maps depends on the accuracy and spatial coverage of air quality measurements. Currently, the maps use only ground-based measurements, which have significant gaps in coverage in some parts of the United States. As a result, contoured AQI levels have high uncertainty in regions far from monitors. To improve the usefulness of air quality maps, scientists at EPA, Dalhousie University, and Sonoma Technology, Inc., in collaboration with the National Aeronautics and Space Administration (NASA) and the National Oceanic and Atmospheric Administration (NOAA), have completed a project to incorporate satellite-estimated surface PM2.5 concentrations into the maps via the AirNow Satellite Data Processor (ASDP). These satellite estimates are derived using NASA/NOAA satellite aerosol optical depth (AOD) retrievals and GEOS-Chem modeled ratios of surface PM2.5 concentrations to AOD. GEOS-Chem is a three-dimensional chemical transport model for atmospheric composition driven by meteorological input from the Goddard Earth Observing System (GEOS). The ASDP can fuse multiple PM2.5 concentration data sets to generate AQI maps with improved spatial coverage. The goals of ASDP are to provide more detailed AQI information in monitor-sparse locations and to augment monitor-dense locations with more information. The ASDP system uses a weighted-average approach using uncertainty information about each data set. Recent improvements in the estimation of the uncertainty of interpolated ground-based monitor data have allowed for a more complete characterization of the uncertainty of the surface measurements. We will present a statistical analysis for 2010-2012 of the ASDP predictions of PM2.5 focusing on performance at validation sites. In addition, we will present several case studies evaluating the ASDP's performance for multiple regions and seasons, focusing specifically on days when large spatial gradients in AQI and wildfire smoke impacts were observed.

Global ozone and air quality: a multi-model assessment of risks to human health and crops

NASA Astrophysics Data System (ADS)

Ellingsen, K.; Gauss, M.; van Dingenen, R.; Dentener, F. J.; Emberson, L.; Fiore, A. M.; Schultz, M. G.; Stevenson, D. S.; Ashmore, M. R.; Atherton, C. S.; Bergmann, D. J.; Bey, I.; Butler, T.; Drevet, J.; Eskes, H.; Hauglustaine, D. A.; Isaksen, I. S. A.; Horowitz, L. W.; Krol, M.; Lamarque, J. F.; Lawrence, M. G.; van Noije, T.; Pyle, J.; Rast, S.; Rodriguez, J.; Savage, N.; Strahan, S.; Sudo, K.; Szopa, S.; Wild, O.

2008-02-01

Within ACCENT, a European Network of Excellence, eighteen atmospheric models from the U.S., Europe, and Japan calculated present (2000) and future (2030) concentrations of ozone at the Earth's surface with hourly temporal resolution. Comparison of model results with surface ozone measurements in 14 world regions indicates that levels and seasonality of surface ozone in North America and Europe are characterized well by global models, with annual average biases typically within 5-10 nmol/mol. However, comparison with rather sparse observations over some regions suggest that most models overestimate annual ozone by 15-20 nmol/mol in some locations. Two scenarios from the International Institute for Applied Systems Analysis (IIASA) and one from the Intergovernmental Panel on Climate Change Special Report on Emissions Scenarios (IPCC SRES) have been implemented in the models. This study focuses on changes in near-surface ozone and their effects on human health and vegetation. Different indices and air quality standards are used to characterise air quality. We show that often the calculated changes in the different indices are closely inter-related. Indices using lower thresholds are more consistent between the models, and are recommended for global model analysis. Our analysis indicates that currently about two-thirds of the regions considered do not meet health air quality standards, whereas only 2-4 regions remain below the threshold. Calculated air quality exceedances show moderate deterioration by 2030 if current emissions legislation is followed and slight improvements if current emissions reduction technology is used optimally. For the "business as usual" scenario severe air quality problems are predicted. We show that model simulations of air quality indices are particularly sensitive to how well ozone is represented, and improved accuracy is needed for future projections. Additional measurements are needed to allow a more quantitative assessment of the risks to human health and vegetation from changing levels of surface ozone.

Air Quality and Health Benefits of China's Recent Stringent Environmental Policy

NASA Astrophysics Data System (ADS)

Zheng, Y.; Xue, T.; Zhang, Q.; Geng, G.; He, K.

2016-12-01

Aggressive emission control measures were taken by China's central and local governments after the promulgation of the "Air Pollution Prevention and Control Action Plan" in 2013. We evaluated the air quality and health benefits of this ever most stringent air pollution control policy during 2013-2015 by utilizing a two-stage data fusion model and newly-developed cause-specific integrated exposure-response functions (IER) developed for the Global Burden of Disease (GBD). The two-stage data fusion model predicts spatiotemporal continuous PM2.5 (particulate matter with aerodynamic diameter less than 2.5 µm) concentrations by integrating satellite-derived aerosol optical depth (AOD) measurements, PM2.5 concentrations from measurement and air quality model, and other ancillary information. During the years of analysis, PM2.5 concentration dropped significantly on national average and over heavily polluted regions as identified by Mann-Kendall analysis. The national PM2.5-attributable mortality decreased by 72.8 (95% CI: 59.4, 85.2) thousand (6%) from 1.23 (95% CI: 1.06, 1.39) million in 2013 to 1.15 (95% CI: 0.98, 1.31) million in 2015 due to considerable reduction (i.e. 18%) of population-weighted PM2.5 from 61.4 to 50.5 µg/m3. Meteorological variations between 2013 and 2015 were estimated to raise the PM2.5 levels by 0.24 µg/m3 and national mortality by 2.1 (95% CI: 1.6, 2.6) thousand through sensitivity tests, which implies the dominant role of anthropogenic impacts on PM2.5 abatement and attributable mortality reduction. Our study affirms the effectiveness of China's recent air quality policy, however, due to the possible supralinear shape of C-R functions, health benefits induced by air quality improvement in these years are limited. We therefore appeal for continuous implementation of current policies and further stringent measures from both air quality improvement and public health protection perspectives.

The use of LIDAR to characterize aircraft exhaust plumes

DOT National Transportation Integrated Search

2003-06-22

Aircraft emissions are a growing concern for the FAA, airports, and the community. U.S. : and international air quality models were previously unable to accurately predict initial : plume dispersion and the resulting pollutant concentrations because ...

Japan's efforts to promote global health using satellite remote sensing data from the Japan Aerospace Exploration Agency for prediction of infectious diseases and air quality.

PubMed

Igarashi, Tamotsu; Kuze, Akihiko; Sobue, Shinichi; Yamamoto, Aya; Yamamoto, Kazuhide; Oyoshi, Kei; Imaoka, Keiji; Fukuda, Toru

2014-12-01

In this paper we review the status of new applications research of the Japanese Aerospace Exploration Agency (JAXA) for global health promotion using information derived from Earth observation data by satellites in cooperation with inter-disciplinary collaborators. Current research effort at JAXA to promote global public health is focused primarily on the use of remote sensing to address two themes: (i) prediction models for malaria and cholera in Kenya, Africa; and (ii) air quality assessment of small, particulate matter (PM2.5), nitrogen dioxide (NO2) and ozone (O3). Respiratory and cardivascular diseases constitute cross-boundary public health risk issues on a global scale. The authors report here on results of current of a collaborative research to call attention to the need to take preventive measures against threats to public health using newly arising remote sensing information from space.

Future directions of meteorology related to air-quality research.

PubMed

Seaman, Nelson L

2003-06-01

Meteorology is one of the major factors contributing to air-pollution episodes. More accurate representation of meteorological fields has been possible in recent years through the use of remote sensing systems, high-speed computers and fine-mesh meteorological models. Over the next 5-20 years, better meteorological inputs for air quality studies will depend on making better use of a wealth of new remotely sensed observations in more advanced data assimilation systems. However, for fine mesh models to be successful, parameterizations used to represent physical processes must be redesigned to be more precise and better adapted for the scales at which they will be applied. Candidates for significant overhaul include schemes to represent turbulence, deep convection, shallow clouds, and land-surface processes. Improvements in the meteorological observing systems, data assimilation and modeling, coupled with advancements in air-chemistry modeling, will soon lead to operational forecasting of air quality in the US. Predictive capabilities can be expected to grow rapidly over the next decade. This will open the way for a number of valuable new services and strategies, including better warnings of unhealthy atmospheric conditions, event-dependent emissions restrictions, and now casting support for homeland security in the event of toxic releases into the atmosphere.

Satellite data driven modeling system for predicting air quality and visibility during wildfire and prescribed burn events

NASA Astrophysics Data System (ADS)

Nair, U. S.; Keiser, K.; Wu, Y.; Maskey, M.; Berendes, D.; Glass, P.; Dhakal, A.; Christopher, S. A.

2012-12-01

The Alabama Forestry Commission (AFC) is responsible for wildfire control and also prescribed burn management in the state of Alabama. Visibility and air quality degradation resulting from smoke are two pieces of information that are crucial for this activity. Currently the tools available to AFC are the dispersion index available from the National Weather Service and also surface smoke concentrations. The former provides broad guidance for prescribed burning activities but does not provide specific information regarding smoke transport, areas affected and quantification of air quality and visibility degradation. While the NOAA operational air quality guidance includes surface smoke concentrations from existing fire events, it does not account for contributions from background aerosols, which are important for the southeastern region including Alabama. Also lacking is the quantification of visibility. The University of Alabama in Huntsville has developed a state-of-the-art integrated modeling system to address these concerns. This system based on the Community Air Quality Modeling System (CMAQ) that ingests satellite derived smoke emissions and also assimilates NASA MODIS derived aerosol optical thickness. In addition, this operational modeling system also simulates the impact of potential prescribed burn events based on location information derived from the AFC prescribed burn permit database. A lagrangian model is used to simulate smoke plumes for the prescribed burns requests. The combined air quality and visibility degradation resulting from these smoke plumes and background aerosols is computed and the information is made available through a web based decision support system utilizing open source GIS components. This system provides information regarding intersections between highways and other critical facilities such as old age homes, hospitals and schools. The system also includes satellite detected fire locations and other satellite derived datasets relevant for fire and smoke management.

The Effects of Urban Form on Ambient Air Pollution and Public Health Risk: A Case Study in Raleigh, North Carolina

PubMed Central

Rodriguez, Daniel A.; Huegy, Joseph; Gibson, Jacqueline MacDonald

2014-01-01

Since motor vehicles are a major air pollution source, urban designs that decrease private automobile use could improve air quality and decrease air pollution health risks. Yet, the relationships among urban form, air quality, and health are complex and not fully understood. To explore these relationships, we model the effects of three alternative development scenarios on annual average fine particulate matter (PM2.5) concentrations in ambient air and associated health risks from PM2.5 exposure in North Carolina’s Raleigh-Durham-Chapel Hill area. We integrate transportation demand, land-use regression, and health risk assessment models to predict air quality and health impacts for three development scenarios: current conditions, compact development, and sprawling development. Compact development slightly decreases (−0.2%) point estimates of regional annual average PM2.5 concentrations, while sprawling development slightly increases (+1%) concentrations. However, point estimates of health impacts are in opposite directions: compact development increases (+39%) and sprawling development decreases (−33%) PM2.5-attributable mortality. Further, compactness increases local variation in PM2.5 concentrations and increases the severity of local air pollution hotspots. Hence, this research suggests that while compact development may improve air quality from a regional perspective, it may also increase the concentration of PM2.5 in local hotspots and increase population exposure to PM2.5. Health effects may be magnified if compact neighborhoods and PM2.5 hotspots are spatially co-located. We conclude that compactness alone is an insufficient means of reducing the public health impacts of transportation emissions in automobile-dependent regions. Rather, additional measures are needed to decrease automobile dependence and the health risks of transportation emissions. PMID:25490890

A hybrid modeling with data assimilation to evaluate human exposure level

NASA Astrophysics Data System (ADS)

Koo, Y. S.; Cheong, H. K.; Choi, D.; Kim, A. L.; Yun, H. Y.

2015-12-01

Exposure models are designed to better represent human contact with PM (Particulate Matter) and other air pollutants such as CO, SO2, O3, and NO2. The exposure concentrations of the air pollutants to human are determined by global and regional long range transport of global and regional scales from Europe and China as well as local emissions from urban and road vehicle sources. To assess the exposure level in detail, the multiple scale influence from background to local sources should be considered. A hybrid air quality modeling methodology combing a grid-based chemical transport model with a local plume dispersion model was used to provide spatially and temporally resolved air quality concentration for human exposure levels in Korea. In the hybrid modeling approach, concentrations from a grid-based chemical transport model and a local plume dispersion model are added to provide contributions from photochemical interactions, long-range (regional) transport and local-scale dispersion. The CAMx (Comprehensive Air quality Model with Extensions was used for the background concentrations from anthropogenic and natural emissions in East Asia including Korea while the road dispersion by vehicle emission was calculated by CALPUFF model. The total exposure level of the pollutants was finally assessed by summing the background and road contributions. In the hybrid modeling, the data assimilation method based on the optimal interpolation was applied to overcome the discrepancies between the model predicted concentrations and observations. The air quality data from the air quality monitoring stations in Korea. The spatial resolution of the hybrid model was 50m for the Seoul Metropolitan Ares. This example clearly demonstrates that the exposure level could be estimated to the fine scale for the exposure assessment by using the hybrid modeling approach with data assimilation.

Changes in air quality and tropospheric composition due to depletion of stratospheric ozone and interactions with changing climate: implications for human and environmental health.

PubMed

Madronich, S; Shao, M; Wilson, S R; Solomon, K R; Longstreth, J D; Tang, X Y

2015-01-01

UV radiation is an essential driver for the formation of photochemical smog, which includes ground-level ozone and particulate matter (PM). Recent analyses support earlier work showing that poor outdoor air quality is a major environmental hazard as well as quantifying health effects on regional and global scales more accurately. Greater exposure to these pollutants has been linked to increased risks of cardiovascular and respiratory diseases in humans and is associated globally with several million premature deaths per year. Ozone also has adverse effects on yields of crops, leading to loss of billions of US dollars each year. These detrimental effects also may alter biological diversity and affect the function of natural ecosystems. Future air quality will depend mostly on changes in emission of pollutants and their precursors, but changes in UV radiation and climate will contribute as well. Significant reductions in emissions, mainly from the energy and transportation sectors, have already led to improved air quality in many locations. Air quality will continue to improve in those cities/states that can afford controls, and worsen where the regulatory infrastructure is not available. Future changes in UV radiation and climate will alter the rates of formation of ground-level ozone and photochemically-generated particulate matter and must be considered in predictions of air quality. The decrease in UV radiation associated with recovery of stratospheric ozone will, according to recent global atmospheric model simulations, lead to increases in ground-level ozone at most locations. If correct, this will add significantly to future ground-level ozone trends. However, the spatial resolution of these global models is insufficient to inform policy at this time, especially for urban areas. UV radiation affects the atmospheric concentration of hydroxyl radicals, ˙OH, which are responsible for the self-cleaning of the atmosphere. Recent measurements confirm that, on a local scale, ˙OH radicals respond rapidly to changes in UV radiation. However, on large (global) scales, models differ in their predictions by nearly a factor of two, with consequent uncertainties for estimating the atmospheric lifetime and concentrations of key greenhouse gases and air pollutants. Projections of future climate need to consider these uncertainties. No new negative environmental effects of substitutes for ozone depleting substances or their breakdown-products have been identified. However, some substitutes for the ozone depleting substances will continue to contribute to global climate change if concentrations rise above current levels.

The Use of Satellite Remote Sensing in Epidemiological Studies

PubMed Central

Sorek-Hamer, Meytar; Just, Allan C.; Kloog, Itai

2016-01-01

Purpose of review Particulate matter (PM) air pollution is a ubiquitous exposure linked with multiple adverse health outcomes for children and across the life course. The recent development of satellite based remote sensing models for air pollution enables the quantification of these risks and addresses many limitations of previous air pollution research strategies. We review the recent literature on the applications of satellite remote sensing in air quality research, with a focus on their use in epidemiological studies. Recent findings Aerosol optical depth (AOD) is a focus of this review and a significant number of studies show that ground-level PM can be estimated from columnar AOD. Satellite measurements have been found to be an important source of data for PM model-based exposure estimates, and recently have been used in health studies to increase the spatial breadth and temporal resolution of these estimates. Summary It is suggested that satellite-based models improve our understanding of the spatial characteristics of air quality. Although the adoption of satellite-based measures of air quality in health studies is in its infancy, it is rapidly growing. Nevertheless, further investigation is still needed in order to have a better understanding of the AOD contribution to these prediction models in order to use them with higher accuracy in epidemiological studies. PMID:26859287

Satellite remote sensing in epidemiological studies.

PubMed

Sorek-Hamer, Meytar; Just, Allan C; Kloog, Itai

2016-04-01

Particulate matter air pollution is a ubiquitous exposure linked with multiple adverse health outcomes for children and across the life course. The recent development of satellite-based remote-sensing models for air pollution enables the quantification of these risks and addresses many limitations of previous air pollution research strategies. We review the recent literature on the applications of satellite remote sensing in air quality research, with a focus on their use in epidemiological studies. Aerosol optical depth (AOD) is a focus of this review and a significant number of studies show that ground-level particulate matter can be estimated from columnar AOD. Satellite measurements have been found to be an important source of data for particulate matter model-based exposure estimates, and recently have been used in health studies to increase the spatial breadth and temporal resolution of these estimates. It is suggested that satellite-based models improve our understanding of the spatial characteristics of air quality. Although the adoption of satellite-based measures of air quality in health studies is in its infancy, it is rapidly growing. Nevertheless, further investigation is still needed in order to have a better understanding of the AOD contribution to these prediction models in order to use them with higher accuracy in epidemiological studies.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bloomquist, R.G.

District heating and cooling (DHC) can provide multiple opportunities to reduce air emissions associated with space conditioning and electricity generation, which contribute 30% to 50% of all such emissions. When DHC is combined with cogeneration (CHP), maximum reductions in sulfur oxides (SO{sub x}), nitrogen oxides (NO{sub x}), carbon dioxide (CO{sub 2}), particulates, and ozone-depleting chlorofluorocarbon (CFC) refrigerants can most effectively be achieved. Although significant improvements in air quality have been documented in Europe and Scandinavia due to DHC and CHP implementation, accurately predicting such improvements has been difficult. Without acceptable quantification methods, regulatory bodies are reluctant to grant air emissionsmore » credits, and local community leaders are unwilling to invest in DHC and CHP as preferred methods of providing energy or strategies for air quality improvement. The recent development and release of a number of computer models designed specifically to provide quantification of air emissions that can result from DHC and CHP implementation should help provide local, state, and national policymakers with information vital to increasing support and investment in DHC development.« less

Actual measurement, hygrothermal response experiment and growth prediction analysis of microbial contamination of central air conditioning system in Dalian, China

PubMed Central

Lv, Yang; Hu, Guangyao; Wang, Chunyang; Yuan, Wenjie; Wei, Shanshan; Gao, Jiaoqi; Wang, Boyuan; Song, Fangchao

2017-01-01

The microbial contamination of central air conditioning system is one of the important factors that affect the indoor air quality. Actual measurement and analysis were carried out on microbial contamination in central air conditioning system at a venue in Dalian, China. Illumina miseq method was used and three fungal samples of two units were analysed by high throughput sequencing. Results showed that the predominant fungus in air conditioning unit A and B were Candida spp. and Cladosporium spp., and two fungus were further used in the hygrothermal response experiment. Based on the data of Cladosporium in hygrothermal response experiment, this paper used the logistic equation and the Gompertz equation to fit the growth predictive model of Cladosporium genera in different temperature and relative humidity conditions, and the square root model was fitted based on the two environmental factors. In addition, the models were carried on the analysis to verify the accuracy and feasibility of the established model equation. PMID:28367963

Actual measurement, hygrothermal response experiment and growth prediction analysis of microbial contamination of central air conditioning system in Dalian, China.

PubMed

Lv, Yang; Hu, Guangyao; Wang, Chunyang; Yuan, Wenjie; Wei, Shanshan; Gao, Jiaoqi; Wang, Boyuan; Song, Fangchao

2017-04-03

The microbial contamination of central air conditioning system is one of the important factors that affect the indoor air quality. Actual measurement and analysis were carried out on microbial contamination in central air conditioning system at a venue in Dalian, China. Illumina miseq method was used and three fungal samples of two units were analysed by high throughput sequencing. Results showed that the predominant fungus in air conditioning unit A and B were Candida spp. and Cladosporium spp., and two fungus were further used in the hygrothermal response experiment. Based on the data of Cladosporium in hygrothermal response experiment, this paper used the logistic equation and the Gompertz equation to fit the growth predictive model of Cladosporium genera in different temperature and relative humidity conditions, and the square root model was fitted based on the two environmental factors. In addition, the models were carried on the analysis to verify the accuracy and feasibility of the established model equation.

Predicting effects of environmental change on river inflows to ...

EPA Pesticide Factsheets

Estuarine river watersheds provide valued ecosystem services to their surrounding communities including drinking water, fish habitat, and regulation of estuarine water quality. However, the provisioning of these services can be affected by changes in the quantity and quality of river water, such as those caused by altered landscapes or shifting temperatures or precipitation. We used the ecohydrology model, VELMA, in the Trask River watershed to simulate the effects of environmental change scenarios on estuarine river inputs to Tillamook Bay (OR) estuary. The Trask River watershed is 453 km2 and contains extensive agriculture, silviculture, urban, and wetland areas. VELMA was parameterized using existing spatial datasets of elevation, soil type, land use, air temperature, precipitation, river flow, and water quality. Simulated land use change scenarios included alterations in the distribution of the nitrogen-fixing tree species Alnus rubra, and comparisons of varying timber harvest plans. Scenarios involving spatial and temporal shifts in air temperature and precipitation trends were also simulated. Our research demonstrates the utility of ecohydrology models such as VELMA to aid in watershed management decision-making. Model outputs of river water flow, temperature, and nutrient concentrations can be used to predict effects on drinking water quality, salmonid populations, and estuarine water quality. This modeling effort is part of a larger framework of

BlueSky Cloud - rapid infrastructure capacity using Amazon's Cloud for wildfire emergency response

NASA Astrophysics Data System (ADS)

Haderman, M.; Larkin, N. K.; Beach, M.; Cavallaro, A. M.; Stilley, J. C.; DeWinter, J. L.; Craig, K. J.; Raffuse, S. M.

2013-12-01

During peak fire season in the United States, many large wildfires often burn simultaneously across the country. Smoke from these fires can produce air quality emergencies. It is vital that incident commanders, air quality agencies, and public health officials have smoke impact information at their fingertips for evaluating where fires and smoke are and where the smoke will go next. To address the need for this kind of information, the U.S. Forest Service AirFire Team created the BlueSky Framework, a modeling system that predicts concentrations of particle pollution from wildfires. During emergency response, decision makers use BlueSky predictions to make public outreach and evacuation decisions. The models used in BlueSky predictions are computationally intensive, and the peak fire season requires significantly more computer resources than off-peak times. Purchasing enough hardware to run the number of BlueSky Framework runs that are needed during fire season is expensive and leaves idle servers running the majority of the year. The AirFire Team and STI developed BlueSky Cloud to take advantage of Amazon's virtual servers hosted in the cloud. With BlueSky Cloud, as demand increases and decreases, servers can be easily spun up and spun down at a minimal cost. Moving standard BlueSky Framework runs into the Amazon Cloud made it possible for the AirFire Team to rapidly increase the number of BlueSky Framework instances that could be run simultaneously without the costs associated with purchasing and managing servers. In this presentation, we provide an overview of the features of BlueSky Cloud, describe how the system uses Amazon Cloud, and discuss the costs and benefits of moving from privately hosted servers to a cloud-based infrastructure.

A multisensor evaluation of the asymmetric convective model, version 2, in southeast Texas.

PubMed

Kolling, Jenna S; Pleim, Jonathan E; Jeffries, Harvey E; Vizuete, William

2013-01-01

There currently exist a number of planetary boundary layer (PBL) schemes that can represent the effects of turbulence in daytime convective conditions, although these schemes remain a large source of uncertainty in meteorology and air quality model simulations. This study evaluates a recently developed combined local and nonlocal closure PBL scheme, the Asymmetric Convective Model, version 2 (ACM2), against PBL observations taken from radar wind profilers, a ground-based lidar, and multiple daytime radiosonde balloon launches. These observations were compared against predictions of PBLs from the Weather Research and Forecasting (WRF) model version 3.1 with the ACM2 PBL scheme option, and the Fifth-Generation Meteorological Model (MM5) version 3.7.3 with the Eta PBL scheme option that is currently being used to develop ozone control strategies in southeast Texas. MM5 and WRF predictions during the regulatory modeling episode were evaluated on their ability to predict the rise and fall of the PBL during daytime convective conditions across southeastern Texas. The MM5 predicted PBLs consistently underpredicted observations, and were also less than the WRF PBL predictions. The analysis reveals that the MM5 predicted a slower rising and shallower PBL not representative of the daytime urban boundary layer. Alternatively, the WRF model predicted a more accurate PBL evolution improving the root mean square error (RMSE), both temporally and spatially. The WRF model also more accurately predicted vertical profiles of temperature and moisture in the lowest 3 km of the atmosphere. Inspection of median surface temperature and moisture time-series plots revealed higher predicted surface temperatures in WRF and more surface moisture in MM5. These could not be attributed to surface heat fluxes, and thus the differences in performance of the WRF and MM5 models are likely due to the PBL schemes. An accurate depiction of the diurnal evolution of the planetary boundary layer (PBL) is necessary for realistic air quality simulations, and for formulating effective policy. The meteorological model used to support the southeast Texas 03 attainment demonstration made predictions of the PBL that were consistently less than those found in observations. The use of the Asymmetric Convective Model, version 2 (ACM2), predicted taller PBL heights and improved model predictions. A lower predicted PBL height in an air quality model would increase precursor concentrations and change the chemical production of O3 and possibly the response to control strategies.

Impact of chemical lateral boundary conditions in a regional air quality forecast model on surface ozone predictions during stratospheric intrusions

NASA Astrophysics Data System (ADS)

Pendlebury, Diane; Gravel, Sylvie; Moran, Michael D.; Lupu, Alexandru

2018-02-01

A regional air quality forecast model, GEM-MACH, is used to examine the conditions under which a limited-area air quality model can accurately forecast near-surface ozone concentrations during stratospheric intrusions. Periods in 2010 and 2014 with known stratospheric intrusions over North America were modelled using four different ozone lateral boundary conditions obtained from a seasonal climatology, a dynamically-interpolated monthly climatology, global air quality forecasts, and global air quality reanalyses. It is shown that the mean bias and correlation in surface ozone over the course of a season can be improved by using time-varying ozone lateral boundary conditions, particularly through the correct assignment of stratospheric vs. tropospheric ozone along the western lateral boundary (for North America). Part of the improvement in surface ozone forecasts results from improvements in the characterization of near-surface ozone along the lateral boundaries that then directly impact surface locations near the boundaries. However, there is an additional benefit from the correct characterization of the location of the tropopause along the western lateral boundary such that the model can correctly simulate stratospheric intrusions and their associated exchange of ozone from stratosphere to troposphere. Over a three-month period in spring 2010, the mean bias was seen to improve by as much as 5 ppbv and the correlation by 0.1 depending on location, and on the form of the chemical lateral boundary condition.

Exploring the modeling of spatiotemporal variations in ambient air pollution within the land use regression framework: Estimation of PM10 concentrations on a daily basis.

PubMed

Alam, Md Saniul; McNabola, Aonghus

2015-05-01

Estimation of daily average exposure to PM10 (particulate matter with an aerodynamic diameter<10 μm) using the available fixed-site monitoring stations (FSMs) in a city poses a great challenge. This is because typically FSMs are limited in number when considering the spatial representativeness of their measurements and also because statistical models of citywide exposure have yet to be explored in this context. This paper deals with the later aspect of this challenge and extends the widely used land use regression (LUR) approach to deal with temporal changes in air pollution and the influence of transboundary air pollution on short-term variations in PM10. Using the concept of multiple linear regression (MLR) modeling, the average daily concentrations of PM10 in two European cities, Vienna and Dublin, were modeled. Models were initially developed using the standard MLR approach in Vienna using the most recently available data. Efforts were subsequently made to (i) assess the stability of model predictions over time; (ii) explores the applicability of nonparametric regression (NPR) and artificial neural networks (ANNs) to deal with the nonlinearity of input variables. The predictive performance of the MLR models of the both cities was demonstrated to be stable over time and to produce similar results. However, NPR and ANN were found to have more improvement in the predictive performance in both cities. Using ANN produced the highest result, with daily PM10 exposure predicted at R2=66% for Vienna and 51% for Dublin. In addition, two new predictor variables were also assessed for the Dublin model. The variables representing transboundary air pollution and peak traffic count were found to account for 6.5% and 12.7% of the variation in average daily PM10 concentration. The variable representing transboundary air pollution that was derived from air mass history (from back-trajectory analysis) and population density has demonstrated a positive impact on model performance. The implications of this research would suggest that it is possible to produce a model of ambient air quality on a citywide scale using the readily available data. Most European cities typically have a limited FSM network with average daily concentrations of air pollutants as well as available meteorological, traffic, and land-use data. This research highlights that using these data in combination with advanced statistical techniques such as NPR or ANNs will produce reasonably accurate predictions of ambient air quality across a city, including temporal variations. Therefore, this approach reduces the need for additional measurement data to supplement existing historical records and enables a lower-cost method of air pollution model development for practitioners and policy makers.

Integration of Air Quality & Exposure Models for Health Studies

EPA Science Inventory

The presentation describes a new community-scale tool called exposure model for individuals (EMI), which predicts five tiers of individual-level exposure metrics for ambient PM using outdoor concentrations, questionnaires, weather, and time-location information. In this modeling ...

Key Issues for Seamless Integrated Chemistry–Meteorology Modeling

EPA Science Inventory

Online coupled meteorology–atmospheric chemistry models have greatly evolved in recent years. Although mainly developed by the air quality modeling community, these integrated models are also of interest for numerical weather prediction and climate modeling, as they can con...

PROBABILISTIC CHARACTERIZATION OF ATMOSPHERIC TRANSPORT AND DIFFUSION

EPA Science Inventory

The observed scatter of observations about air quality model predictions stems from a combination of naturally occurring stochastic variations that are impossible for any model to explicitly simulate and variations arising from limitations in our knowledge and from imperfect inpu...

Final report: the use of LIDAR to characterize aircraft initial plume characteristics

DOT National Transportation Integrated Search

2004-02-28

Aircraft emissions are a growing concern for the FAA, airports, and the community. U.S. : and international air quality models were previously unable to accurately predict initial : plume dispersion and the resulting pollutant concentrations because ...

National Centers for Environmental Prediction

Science.gov Websites

Products Operational Forecast Graphics Experimental Forecast Graphics Verification and Diagnostics Model PARALLEL/EXPERIMENTAL MODEL FORECAST GRAPHICS OPERATIONAL VERIFICATION / DIAGNOSTICS PARALLEL VERIFICATION Developmental Air Quality Forecasts and Verification Back to Table of Contents 2. PARALLEL/EXPERIMENTAL GRAPHICS

National Centers for Environmental Prediction

Science.gov Websites

Operational Forecast Graphics Experimental Forecast Graphics Verification and Diagnostics Model Configuration /EXPERIMENTAL MODEL FORECAST GRAPHICS OPERATIONAL VERIFICATION / DIAGNOSTICS PARALLEL VERIFICATION / DIAGNOSTICS Developmental Air Quality Forecasts and Verification Back to Table of Contents 2. PARALLEL/EXPERIMENTAL GRAPHICS

Dimethylsulfide Chemistry: Annual, Seasonal, and Spatial Impacts on Sulfate

EPA Science Inventory

We incorporated oceanic emissions and atmospheric chemistry of dimethylsulfide (DMS) into the hemispheric Community Multiscale Air Quality model and performed annual model simulations without and with DMS chemistry. The model without DMS chemistry predicts higher concentrations o...

Evaluation of CALGRID using two different ozone episodes and comparison to UAM results

NASA Astrophysics Data System (ADS)

Kumar, Naresh; Russell, Armistead G.; Tesche, Thomas W.; McNally, Dennis E.

Air quality models serve as the foundation for policy decisions regarding programs designed to improve air quality. The California Air Resources Board Airshed Model (CALGRID) is one of the latest photochemical air quality models developed for assessing ozone control strategies. CALGRID was modified to include the lates CBIV chemical mechanism in place of the original SAPRC mechanism. After modification, a detailed evaluation of CALGRID was carried out using two different ozone episodes. The first evaluation used data obtained during the Southern California Air Quality Study (SCAQS). The second evaluation used data obtained for the September, 1984 SCCCAMP episodes in the South Central Coast Air Basin (SCCAB). Model results were compared against observations of O 3, NO, NO 2, and different organic compounds. For the SCCAB episode, the results were also compared with those obtained from the Urban Airshed Model (UAM). Similar to other studies, the ozone predictions from the SCAQS application were biased low, as were various ROG components. The reason for this can be linked to the under-representation of ROG and CO in the emissions inventory. For the SCCAB episode, both the UAM and CALGRID models significantly underestimated NO and NO 2 concentrations. The two models slightly underestimated ozone concentrations above approximately 9 pphm on the third and last day of the simulation. Sensitivity experiments were performed for both the studies. It was found that both CALGRID and UAM are strongly sensitive to the boundary conditions and moderately sensitive to the emissions for the episodes modeled.

Evaluation of data assimilation techniques for a mesoscale meteorological model and their effects on air quality model results

NASA Astrophysics Data System (ADS)

Amicarelli, A.; Gariazzo, C.; Finardi, S.; Pelliccioni, A.; Silibello, C.

2008-05-01

Data assimilation techniques are methods to limit the growth of errors in a dynamical model by allowing observations distributed in space and time to force (nudge) model solutions. They have become common for meteorological model applications in recent years, especially to enhance weather forecast and to support air-quality studies. In order to investigate the influence of different data assimilation techniques on the meteorological fields produced by RAMS model, and to evaluate their effects on the ozone and PM10 concentrations predicted by FARM model, several numeric experiments were conducted over the urban area of Rome, Italy, during a summer episode.

Measurement and prediction of indoor air quality using a breathing thermal manikin.

PubMed

Melikov, A; Kaczmarczyk, J

2007-02-01

The analyses performed in this paper reveal that a breathing thermal manikin with realistic simulation of respiration including breathing cycle, pulmonary ventilation rate, frequency and breathing mode, gas concentration, humidity and temperature of exhaled air and human body shape and surface temperature is sensitive enough to perform reliable measurement of characteristics of air as inhaled by occupants. The temperature, humidity, and pollution concentration in the inhaled air can be measured accurately with a thermal manikin without breathing simulation if they are measured at the upper lip at a distance of <0.01 m from the face. Body surface temperature, shape and posture as well as clothing insulation have impact on the measured inhaled air parameters. Proper simulation of breathing, especially of exhalation, is needed for studying the transport of exhaled air between occupants. A method for predicting air acceptability based on inhaled air parameters and known exposure-response relationships established in experiments with human subjects is suggested. Recommendations for optimal simulation of human breathing by means of a breathing thermal manikin when studying pollution concentration, temperature and humidity of the inhaled air as well as the transport of exhaled air (which may carry infectious agents) between occupants are outlined. In order to compare results obtained with breathing thermal manikins, their nose and mouth geometry should be standardized.

Field validation of sound mitigation models and air pollutant emission testing in support of missile motor disposal activities.

PubMed

McFarland, Michael J; Palmer, Glenn R; Kordich, Micheal M; Pollet, Dean A; Jensen, James A; Lindsay, Mitchell H

2005-08-01

The U.S. Department of Defense approved activities conducted at the Utah Test and Training Range (UTTR) include both operational readiness test firing of intercontinental ballistic missile motors as well as the destruction of obsolete or otherwise unusable intercontinental ballistic missile motors through open burn/open detonation (OB/ OD). Within the Utah Division of Air Quality, these activities have been identified as having the potential to generate unacceptable noise levels, as well as significant amounts of hazardous air pollutants. Hill Air Force Base, UT, has completed a series of field tests at the UTTR in which sound-monitoring surveillance of OB/OD activities was conducted to validate the Sound Intensity Prediction System (SIPS) model. Using results generated by the SIPS model to support the decision to detonate, the UTTR successfully disposed of missile motors having an aggregate net explosive weight (NEW) of 56,500 lbs without generating adverse noise levels within populated areas. These results suggest that, under appropriate conditions, missile motors of even larger NEW may be detonated without exceeding regulatory noise limits. In conjunction with collecting noise monitoring data, air quality data was collected to support the development of air emission factors for both static missile motor firings and OB/OD activities. Through the installation of 15 ground-based air samplers, the generation of combustion fixed gases, hazardous air pollutants, and chlorides were monitored during the 56,500-lb NEW detonation event. Comparison of field measurements to predictions generated from the U.S. Navy's energetic combustion pollutant formation model, POLU4WN, indicated that, as the detonation fireball expanded from ground zero, organic compounds as well as carbon monoxide continued to oxidize as the hot gases reacted with ambient air. Hazardous air pollutant analysis of air samplers confirmed the presence of chloromethane, benzene, toluene, 1,2-propadiene, and 2-methyl-l-propene, whereas the absence of hydrogen chloride gas suggested that free chlorine is not generated during the combustion process.

Air pollution and blood lipid markers levels: Estimating short and long-term effects on elderly hypertension inpatients complicated with or without type 2 diabetes.

PubMed

Xiao, Sanhua; Liu, Ranran; Wei, Youxiu; Feng, Lin; Lv, Xuemin; Tang, Fei

2016-08-01

With the development of society and the economy, many Chinese cities are shrouded in pollution haze for much of the year. Scientific studies have identified various adverse effects of air pollutants on human beings. However, the relationships between air pollution and blood lipid levels are still unclear. The objective of this study is to explore the short and long-term effects of air pollution on eight blood lipid markers among elderly hypertension inpatients complicated with or without type 2 diabetes (T2D). Blood lipid markers which met the pre-established inclusion criteria were exported from the medical record system. Air pollution data were acquired from the official environmental protection website. Associations between the air quality index and the blood lipid indexes were analyzed by one-way ANOVA and further Bonferroni correction. In an exposure time of 7 days or longer, blood lipid markers were somewhat affected by poor air quality. However, the results could not predict whether atherosclerosis would be promoted or inhibited by poorer air condition. Changes of blood lipid markers of hypertension inpatients with or without T2D were not completely the same, but no blood lipid markers had an opposite trend between the two populations. The air quality index was associated with changes to blood lipid markers to some extent in a population of hypertension inpatients with or without T2D. Further studies are needed to investigate the potential mechanism by which air pollutants induce blood lipids changes. Copyright © 2016 Elsevier Ltd. All rights reserved.

Human-model hybrid Korean air quality forecasting system.

PubMed

Chang, Lim-Seok; Cho, Ara; Park, Hyunju; Nam, Kipyo; Kim, Deokrae; Hong, Ji-Hyoung; Song, Chang-Keun

2016-09-01

The Korean national air quality forecasting system, consisting of the Weather Research and Forecasting, the Sparse Matrix Operator Kernel Emissions, and the Community Modeling and Analysis (CMAQ), commenced from August 31, 2013 with target pollutants of particulate matters (PM) and ozone. Factors contributing to PM forecasting accuracy include CMAQ inputs of meteorological field and emissions, forecasters' capacity, and inherent CMAQ limit. Four numerical experiments were conducted including two global meteorological inputs from the Global Forecast System (GFS) and the Unified Model (UM), two emissions from the Model Intercomparison Study Asia (MICS-Asia) and the Intercontinental Chemical Transport Experiment (INTEX-B) for the Northeast Asia with Clear Air Policy Support System (CAPSS) for South Korea, and data assimilation of the Monitoring Atmospheric Composition and Climate (MACC). Significant PM underpredictions by using both emissions were found for PM mass and major components (sulfate and organic carbon). CMAQ predicts PM2.5 much better than PM10 (NMB of PM2.5: -20~-25%, PM10: -43~-47%). Forecasters' error usually occurred at the next day of high PM event. Once CMAQ fails to predict high PM event the day before, forecasters are likely to dismiss the model predictions on the next day which turns out to be true. The best combination of CMAQ inputs is the set of UM global meteorological field, MICS-Asia and CAPSS 2010 emissions with the NMB of -12.3%, the RMSE of 16.6μ/m(3) and the R(2) of 0.68. By using MACC data as an initial and boundary condition, the performance skill of CMAQ would be improved, especially in the case of undefined coarse emission. A variety of methods such as ensemble and data assimilation are considered to improve further the accuracy of air quality forecasting, especially for high PM events to be comparable to for all cases. The growing utilization of the air quality forecast induced the public strongly to demand that the accuracy of the national forecasting be improved. In this study, we investigated the problems in the current forecasting as well as various alternatives to solve the problems. Such efforts to improve the accuracy of the forecast are expected to contribute to the protection of public health by increasing the availability of the forecast system.

2010-2011 Performance of the AirNow Satellite Data Processor

NASA Astrophysics Data System (ADS)

Pasch, A. N.; DeWinter, J. L.; Haderman, M. D.; van Donkelaar, A.; Martin, R. V.; Szykman, J.; White, J. E.; Dickerson, P.; Zahn, P. H.; Dye, T. S.

2012-12-01

The U.S. Environmental Protection Agency's (EPA) AirNow program provides maps of real time hourly Air Quality Index (AQI) conditions and daily AQI forecasts nationwide (http://www.airnow.gov). The public uses these maps to make health-based decisions. The usefulness of the AirNow air quality maps depends on the accuracy and spatial coverage of air quality measurements. Currently, the maps use only ground-based measurements, which have significant gaps in coverage in some parts of the United States. As a result, contoured AQI levels have high uncertainty in regions far from monitors. To improve the usefulness of air quality maps, scientists at EPA, Dalhousie University, and Sonoma Technology, Inc. have been working in collaboration with the National Aeronautics and Space Administration (NASA) and the National Oceanic and Atmospheric Administration (NOAA) to incorporate satellite-estimated surface PM2.5 concentrations into the maps via the AirNow Satellite Data Processor (ASDP). These satellite estimates are derived using NASA/NOAA satellite aerosol optical depth (AOD) retrievals and GEOS-Chem modeled ratios of surface PM2.5 concentrations to AOD. GEOS-Chem is a three-dimensional chemical transport model for atmospheric composition driven by meteorological input from the Goddard Earth Observing System (GOES). The ASDP can fuse multiple PM2.5 concentration data sets to generate AQI maps with improved spatial coverage. The goal of ASDP is to provide more detailed AQI information in monitor-sparse locations and augment monitor-dense locations with more information. We will present a statistical analysis for 2010-2011 of the ASDP predictions of PM2.5 focusing on performance at validation sites. In addition, we will present several case studies evaluating the ASDP's performance for multiple regions and seasons, focusing specifically on days when large spatial gradients in AQI and wildfire smoke impact were observed.

Methodology for analyzing environmental quality indicators in a dynamic operating room environment.

PubMed

Gormley, Thomas; Markel, Troy A; Jones, Howard W; Wagner, Jennifer; Greeley, Damon; Clarke, James H; Abkowitz, Mark; Ostojic, John

2017-04-01

Sufficient quantities of quality air and controlled, unidirectional flow are important elements in providing a safe building environment for operating rooms. To make dynamic assessments of an operating room environment, a validated method of testing the multiple factors influencing the air quality in health care settings needed to be constructed. These include the following: temperature, humidity, particle load, number of microbial contaminants, pressurization, air velocity, and air distribution. The team developed the name environmental quality indicators (EQIs) to describe the overall air quality based on the actual measurements of these properties taken during the mock surgical procedures. These indicators were measured at 3 different hospitals during mock surgical procedures to simulate actual operating room conditions. EQIs included microbial assessments at the operating table and the back instrument table and real-time analysis of particle counts at 9 different defined locations in the operating suites. Air velocities were measured at the face of the supply diffusers, at the sterile field, at the back table, and at a return grille. The testing protocol provided consistent and comparable measurements of air quality indicators between institutions. At 20 air changes per hour (ACH), and an average temperature of 66.3°F, the median of the microbial contaminants for the 3 operating room sites ranged from 3-22 colony forming units (CFU)/m 3 at the sterile field and 5-27 CFU/m 3 at the back table. At 20 ACH, the median levels of the 0.5-µm particles at the 3 sites were 85,079, 85,325, and 912,232 in particles per cubic meter, with a predictable increase in particle load in the non-high-efficiency particulate air-filtered operating room site. Using a comparison with cleanroom standards, the microbial and particle counts in all 3 operating rooms were equivalent to International Organization for Standardization classifications 7 and 8 during the mock surgical procedures. The EQI protocol was measurable and repeatable and therefore can be safely used to evaluate air quality within the health care environment to provide guidance for operational practices and regulatory requirements. Copyright © 2017 Association for Professionals in Infection Control and Epidemiology, Inc. Published by Elsevier Inc. All rights reserved.

An Elevated Reservoir of Air Pollutants over the Mid-Atlantic States During the 2011 DISCOVER-AQ Campaign: Airborne Measurements and Numerical Simulations

NASA Technical Reports Server (NTRS)

He, Hao; Loughner, Christopher P.; Stehr, Jeffrey W.; Arkinson, Heather L.; Brent, Lacey C.; Follette-Cook, Melanie B.; Tzortziou, Maria A.; Pickering, Kenneth E.; Thompson, Anne M.; Martins, Douglas K.;

Evaluation of the CONSUME and FOFEM fuel consumption models in pine and mixed hardwood forests of the eastern United States

Treesearch

Susan J. Prichard; Eva C. Karau; Roger D. Ottmar; Maureen C. Kennedy; James B. Cronan; Clinton S. Wright; Robert E. Keane

2014-01-01

Reliable predictions of fuel consumption are critical in the eastern United States (US), where prescribed burning is frequently applied to forests and air quality is of increasing concern. CONSUME and the First Order Fire Effects Model (FOFEM), predictive models developed to estimate fuel consumption and emissions from wildland fires, have not been systematically...

Analysis and comparative evaluation of AIRPOL-4.

DOT National Transportation Integrated Search

1975-01-01

AIRPOL-4, an air quality prediction model, has been developed by the Virginia Highway and Transportation Research Council for use in complying with the requirements contained in the Federal Aid Program Manual, Vol. 7, Ch. 7, Section 9, November 1, 19...

Dimethylsulfide chemistry: annual, seasonal, and spatial impacts on SO_4^(2-)

EPA Science Inventory

We incorporated oceanic emissions and atmospheric chemistry of dimethylsulfide (DMS) into the hemispheric Community Multiscale Air Quality model and performed annual model simulations without and with DMS chemistry. The model without DMS chemistry predicts higher concentrations o...

Optimum Installation of Sorptive Building Materials Using Contribution Ratio of Pollution Source for Improvement of Indoor Air Quality.

PubMed

Park, Seonghyun; Seo, Janghoo

2016-04-01

Reinforcing the insulation and airtightness of buildings and the use of building materials containing new chemical substances have caused indoor air quality problems. Use of sorptive building materials along with removal of pollutants, constant ventilation, bake-out, etc. are gaining attention in Korea and Japan as methods for improving such indoor air quality problems. On the other hand, sorptive building materials are considered a passive method of reducing the concentration of pollutants, and their application should be reviewed in the early stages. Thus, in this research, activated carbon was prepared as a sorptive building material. Then, computational fluid dynamics (CFD) was conducted, and a method for optimal installation of sorptive building materials was derived according to the indoor environment using the contribution ratio of pollution source (CRP) index. The results show that a method for optimal installation of sorptive building materials can be derived by predicting the contribution ratio of pollutant sources according to the CRP index.

Impact assessment of PM10 cement plants emissions on urban air quality using the SCIPUFF dispersion model.

PubMed

Leone, Vincenzo; Cervone, Guido; Iovino, Pasquale

2016-09-01

The Second-order Closure Integrated Puff (SCIPUFF) model was used to study the impact on urban air quality caused by two cement plants emissions located near the city of Caserta, Italy, during the entire year of 2015. The simulated and observed PM10 concentrations were compared using three monitoring stations located in urban and sub-urban area of Caserta city. Both simulated and observed concentrations are shown to be highest in winter, lower in autumn and spring and lowest in summer. Model results generally follow the pattern of the observed concentrations but have a systematic under-prediction of the concentration values. Measures of the bias, NMSE and RMSE indicate a good correlation between observed and estimated values. The SCIPUFF model data analysis suggest that the cement plants are major sources for the measured PM10 values and are responsible for the deterioration of the urban air quality in the city of Caserta.

Space Shuttle Environmental Effects: The First 5 Flights

NASA Technical Reports Server (NTRS)

Potter, A. (Editor)

1983-01-01

Environmental effects associated with the first five Space Shuttle flights were monitored by the National Aeronautics and Space Administration (NASA) and the U.S. Air Force (USAF). Results and interpretations from this effort were reported at the December 1982 joint NASA-USAF conference. The conference proceedings are presented in this document. Most of the monitoring activity was focused on the launch cloud, emphasizing surface effects on the biota and air quality, model prediction of surface concentrations of HCl gas and Al2O3 dust, and airborne measurements of cloud composition. In general, assessments and predictions made in the April 1978 Final Environmental Impact Statement for the Space Shuttle Program were verified. Fallout of acidic mist and dust within 3 mi to 5 mi of the launch pad was the only unexpected effect of the launch. Atomization of deluge water in the Shuttle exhaust is considered to be the most probable cause of this effect. Sonic booms were monitored for several landings at Edwards Air Force Base, California; results agreed well with model predictions.

Fine Particulate Matter and Cardiovascular Disease ...

EPA Pesticide Factsheets

Background Adverse cardiovascular events have been linked with PM2.5 exposure obtained primarily from air quality monitors, which rarely co-locate with participant residences. Modeled PM2.5 predictions at finer resolution may more accurately predict residential exposure; however few studies have compared results across different exposure assessment methods. Methods We utilized a cohort of 5679 patients who had undergone a cardiac catheterization between 2002–2009 and resided in NC. Exposure to PM2.5 for the year prior to catheterization was estimated using data from air quality monitors (AQS), Community Multiscale Air Quality (CMAQ) fused models at the census tract and 12 km spatial resolutions, and satellite-based models at 10 km and 1 km resolutions. Case status was either a coronary artery disease (CAD) index >23 or a recent myocardial infarction (MI). Logistic regression was used to model odds of having CAD or an MI with each 1-unit (μg/m3) increase in PM2.5, adjusting for sex, race, smoking status, socioeconomic status, and urban/rural status. Results We found that the elevated odds for CAD>23 and MI were nearly equivalent for all exposure assessment methods. One difference was that data from AQS and the census tract CMAQ showed a rural/urban difference in relative risk, which was not apparent with the satellite or 12 km-CMAQ models. Conclusions

Correlation analysis of air pollutant index levels and dengue cases across five different zones in Selangor, Malaysia.

PubMed

Thiruchelvam, Loshini; Dass, Sarat C; Zaki, Rafdzah; Yahya, Abqariyah; Asirvadam, Vijanth S

2018-05-07

This study investigated the potential relationship between dengue cases and air quality - as measured by the Air Pollution Index (API) for five zones in the state of Selangor, Malaysia. Dengue case patterns can be learned using prediction models based on feedback (lagged terms). However, the question whether air quality affects dengue cases is still not thoroughly investigated based on such feedback models. This work developed dengue prediction models using the autoregressive integrated moving average (ARIMA) and ARIMA with an exogeneous variable (ARIMAX) time series methodologies with API as the exogeneous variable. The Box Jenkins approach based on maximum likelihood was used for analysis as it gives effective model estimates and prediction. Three stages of model comparison were carried out for each zone: first with ARIMA models without API, then ARIMAX models with API data from the API station for that zone and finally, ARIMAX models with API data from the zone and spatially neighbouring zones. Bayesian Information Criterion (BIC) gives goodness-of-fit versus parsimony comparisons between all elicited models. Our study found that ARIMA models, with the lowest BIC value, outperformed the rest in all five zones. The BIC values for the zone of Kuala Selangor were -800.66, -796.22, and -790.5229, respectively, for ARIMA only, ARIMAX with single API component and ARIMAX with API components from its zone and spatially neighbouring zones. Therefore, we concluded that API levels, either temporally for each zone or spatio- temporally based on neighbouring zones, do not have a significant effect on dengue cases.

Temporal and spatial variation in allocating annual traffic activity across an urban region and implications for air quality assessments

PubMed Central

Batterman, Stuart

2015-01-01

Patterns of traffic activity, including changes in the volume and speed of vehicles, vary over time and across urban areas and can substantially affect vehicle emissions of air pollutants. Time-resolved activity at the street scale typically is derived using temporal allocation factors (TAFs) that allow the development of emissions inventories needed to predict concentrations of traffic-related air pollutants. This study examines the spatial and temporal variation of TAFs, and characterizes prediction errors resulting from their use. Methods are presented to estimate TAFs and their spatial and temporal variability and used to analyze total, commercial and non-commercial traffic in the Detroit, Michigan, U.S. metropolitan area. The variability of total volume estimates, quantified by the coefficient of variation (COV) representing the percentage departure from expected hourly volume, was 21, 33, 24 and 33% for weekdays, Saturdays, Sundays and holidays, respectively. Prediction errors mostly resulted from hour-to-hour variability on weekdays and Saturdays, and from day-to-day variability on Sundays and holidays. Spatial variability was limited across the study roads, most of which were large freeways. Commercial traffic had different temporal patterns and greater variability than noncommercial vehicle traffic, e.g., the weekday variability of hourly commercial volume was 28%. The results indicate that TAFs for a metropolitan region can provide reasonably accurate estimates of hourly vehicle volume on major roads. While vehicle volume is only one of many factors that govern on-road emission rates, air quality analyses would be strengthened by incorporating information regarding the uncertainty and variability of traffic activity. PMID:26688671

Role of future scenarios in understanding deep uncertainty in ...

EPA Pesticide Factsheets

The environment and its interactions with human systems, whether economic, social or political, are complex. Relevant drivers may disrupt system dynamics in unforeseen ways, making it difficult to predict future conditions. This kind of deep uncertainty presents a challenge to organizations faced with making decisions about the future, including those involved in air quality management. Scenario Planning is a structured process that involves the development of narratives describing alternative future states of the world, designed to differ with respect to the most critical and uncertain drivers. The resulting scenarios are then used to understand the consequences of those futures and to prepare for them with robust management strategies. We demonstrate a novel air quality management application of Scenario Planning. Through a series of workshops, important air quality drivers were identified. The most critical and uncertain drivers were found to be “technological development” and “change in societal paradigms.” These drivers were used as a basis to develop four distinct scenario storylines. The energy and emission implications of each storyline were then modeled using the MARKAL energy system model. NOX and SO2 emissions were found to decrease for all scenarios, largely a response to existing air quality regulations. Future-year emissions differed considerably from one scenario to another, however, with key differentiating factors being transition

Abrupt recent trend changes in atmospheric nitrogen dioxide over the Middle East

PubMed Central

Lelieveld, Jos; Beirle, Steffen; Hörmann, Christoph; Stenchikov, Georgiy; Wagner, Thomas

2015-01-01

Nitrogen oxides, released from fossil fuel use and other combustion processes, affect air quality and climate. From the mid-1990s onward, nitrogen dioxide (NO2) has been monitored from space, and since 2004 with relatively high spatial resolution by the Ozone Monitoring Instrument. Strong upward NO2 trends have been observed over South and East Asia and the Middle East, in particular over major cities. We show, however, that a combination of air quality control and political factors, including economical crisis and armed conflict, has drastically altered the emission landscape of nitrogen oxides in the Middle East. Large changes, including trend reversals, have occurred since about 2010 that could not have been predicted and therefore are at odds with emission scenarios used in projections of air pollution and climate change in the early 21st century. PMID:26601240

Is the perception of clean, humid air indeed affected by cooling the respiratory tract?

NASA Astrophysics Data System (ADS)

Burek, Rudolf; Polednik, Bernard; Guz, Łukasz

2017-07-01

The study aims at determining exposure-response relationships after short exposure to clean air and long exposure to air polluted by people. The impact of water vapor content in the indoor air on its acceptability (ACC) was assessed by the occupants after a short exposure to clean air and an hour-long exposure to increasingly polluted air. The study presents a critical analysis pertaining to the stimulation of olfactory sensations by the air enthalpy suggested in previous models and proposes a new model based on the Weber-Fechner law. Our assumption was that water vapor is the stimulus of olfactory sensations. The model was calibrated and verified in field conditions, in a mechanically ventilated and air conditioned auditorium. Measurements of the air temperature, relative humidity, velocity and CO2 content were carried out; the acceptability of air quality was assessed by 162 untrained students. The subjective assessments and the measurements of the environmental qualities allowed for determining the Weber coefficients and the threshold concentrations of water vapor, as well as for establishing the limitations of the model at short and long exposure to polluted indoor air. The results are in agreement with previous studies. The standard error equals 0.07 for immediate assessments and 0.17 for assessments after adaptation. Based on the model one can predict the ACC assessments of trained and untrained participants.

Photochemical model evaluation of the ground-level ozone impacts on ambient air quality and vegetation health in the Alberta oil sands region: Using present and future emission scenarios

NASA Astrophysics Data System (ADS)

Vijayaraghavan, Krish; Cho, Sunny; Morris, Ralph; Spink, David; Jung, Jaegun; Pauls, Ron; Duffett, Katherine

2016-09-01

One of the potential environmental issues associated with oil sands development is increased ozone formation resulting from NOX and volatile organic compound emissions from bitumen extraction, processing and upgrading. To manage this issue in the Athabasca Oil Sands Region (AOSR) in northeast Alberta, a regional multi-stakeholder group, the Cumulative Environmental Management Association (CEMA), developed an Ozone Management Framework that includes a modelling based assessment component. In this paper, we describe how the Community Multi-scale Air Quality (CMAQ) model was applied to assess potential ground-level ozone formation and impacts on ambient air quality and vegetation health for three different ozone precursor cases in the AOSR. Statistical analysis methods were applied, and the CMAQ performance results met the U.S. EPA model performance goal at all sites. The modelled 4th highest daily maximum 8-h average ozone concentrations in the base and two future year scenarios did not exceed the Canada-wide standard of 65 ppb or the newer Canadian Ambient Air Quality Standards of 63 ppb in 2015 and 62 ppb in 2020. Modelled maximum 1-h ozone concentrations in the study were well below the Alberta Ambient Air Quality Objective of 82 ppb in all three cases. Several ozone vegetation exposure metrics were also evaluated to investigate the potential impact of ground-level ozone on vegetation. The chronic 3-months SUM60 exposure metric is within the CEMA baseline range (0-2000 ppb-hr) everywhere in the AOSR. The AOT40 ozone exposure metric predicted by CMAQ did not exceed the United Nations Economic Commission for Europe (UN/ECE) threshold of concern of 3000 ppb-hr in any of the cases but is just below the threshold in high-end future emissions scenario. In all three emission scenarios, the CMAQ predicted W126 ozone exposure metric is within the CEMA baseline threshold of 4000 ppb-hr. This study outlines the use of photochemical modelling of the impact of an industry (oil sands) on ground-level ozone levels as an air quality management tool in the AOSR. It allows an evaluation of the relationships between the pollutants emitted to the atmosphere and potential ground level ozone concentrations throughout the AOSR thereby extending the spatial coverage of the results beyond the monitoring network and also allowing an assessment of the potential impacts of possible future emission cases.

URBAN MORPHOLOGICAL ANALYSIS FOR MESOSCALE METEOROLOGICAL AND DISPERSION MODELING APPLICATIONS: CURRENT ISSUES

EPA Science Inventory

Representing urban terrain characteristics in mesoscale meteorological and dispersion models is critical to produce accurate predictions of wind flow and temperature fields, air quality, and contaminant transport. A key component of the urban terrain representation is the charac...

Computer models for predicting the probability of violating CO air quality standards : the model SIMCO.

DOT National Transportation Integrated Search

1982-01-01

This report presents the user instructions and data requirements for SIMCO, a combined simulation and probability computer model developed to quantify and evaluate carbon monoxide in roadside environments. The model permits direct determinations of t...

EXAMINATION OF MODEL PREDICTIONS AT DIFFERENT HORIZONTAL GRID RESOLUTIONS

EPA Science Inventory

While fluctuations in meteorological and air quality variables occur on a continuum of spatial scales, the horizontal grid spacing of coupled meteorological and photochemical models sets a lower limit on the spatial scales that they can resolve. However, both computational costs ...

Assessing chemistry schemes and constraints in air quality models used to predict ozone in London against the detailed Master Chemical Mechanism.

PubMed

Malkin, Tamsin L; Heard, Dwayne E; Hood, Christina; Stocker, Jenny; Carruthers, David; MacKenzie, Ian A; Doherty, Ruth M; Vieno, Massimo; Lee, James; Kleffmann, Jörg; Laufs, Sebastian; Whalley, Lisa K

2016-07-18

Air pollution is the environmental factor with the greatest impact on human health in Europe. Understanding the key processes driving air quality across the relevant spatial scales, especially during pollution exceedances and episodes, is essential to provide effective predictions for both policymakers and the public. It is particularly important for policy regulators to understand the drivers of local air quality that can be regulated by national policies versus the contribution from regional pollution transported from mainland Europe or elsewhere. One of the main objectives of the Coupled Urban and Regional processes: Effects on AIR quality (CUREAIR) project is to determine local and regional contributions to ozone events. A detailed zero-dimensional (0-D) box model run with the Master Chemical Mechanism (MCMv3.2) is used as the benchmark model against which the less explicit chemistry mechanisms of the Generic Reaction Set (GRS) and the Common Representative Intermediates (CRIv2-R5) schemes are evaluated. GRS and CRI are used by the Atmospheric Dispersion Modelling System (ADMS-Urban) and the regional chemistry transport model EMEP4UK, respectively. The MCM model uses a near-explicit chemical scheme for the oxidation of volatile organic compounds (VOCs) and is constrained to observations of VOCs, NOx, CO, HONO (nitrous acid), photolysis frequencies and meteorological parameters measured during the ClearfLo (Clean Air for London) campaign. The sensitivity of the less explicit chemistry schemes to different model inputs has been investigated: Constraining GRS to the total VOC observed during ClearfLo as opposed to VOC derived from ADMS-Urban dispersion calculations, including emissions and background concentrations, led to a significant increase (674% during winter) in modelled ozone. The inclusion of HONO chemistry in this mechanism, particularly during wintertime when other radical sources are limited, led to substantial increases in the ozone levels predicted (223%). When the GRS and CRIv2-R5 schemes are run with the equivalent model constraints to the MCM, they are able to reproduce the level of ozone predicted by the near-explicit MCM to within 40% and 20% respectively for the majority of the time. An exception to this trend was observed during pollution episodes experienced in the summer, when anticyclonic conditions favoured increased temperatures and elevated O3. The in situ O3 predicted by the MCM was heavily influenced by biogenic VOCs during these conditions and the low GRS [O3] : MCM [O3] ratio (and low CRIv2-R5 [O3] : MCM [O3] ratio) demonstrates that these less explicit schemes under-represent the full O3 creation potential of these VOCs. To fully assess the influence of the in situ O3 generated from local emissions versus O3 generated upwind of London and advected in, the time since emission (and, hence, how far the real atmosphere is from steady state) must be determined. From estimates of the mean transport time determined from the NOx : NOy ratio observed at North Kensington during the summer and comparison of the O3 predicted by the MCM model after this time, ∼60% of the median observed [O3] could be generated from local emissions. During the warmer conditions experienced during the easterly flows, however, the observed [O3] may be even more heavily influenced by London's emissions.

Effects of climate change on aerosol concentrations in Europe

NASA Astrophysics Data System (ADS)

Megaritis, Athanasios G.; Fountoukis, Christos; Pandis, Spyros N.

2013-04-01

High concentrations of particulate matter less than 2.5 μm in size (PM2.5), ozone and other major constituents of air pollution, have adverse effects on human health, visibility and ecosystems (Seinfeld and Pandis, 2006), and are strongly influenced by meteorology. Emissions control policy is currently made assuming that climate will remain constant in the future. However, climate change over the next decades is expected to be significant (IPCC, 2007) and may impact local and regional air quality. Determining the sensitivity of the concentrations of air pollutants to climate change is an important step toward estimating future air quality. In this study we applied PMCAMx (Fountoukis et al., 2011), a three dimensional chemical transport model, over Europe, in order to quantify the individual effects of various meteorological parameters on fine particulate matter (PM2.5) concentrations. A suite of perturbations in various meteorological factors, such as temperature, wind speed, absolute humidity and precipitation were imposed separately on base case conditions to determine the sensitivities of PM2.5 concentrations and composition to these parameters. Different simulation periods (summer, autumn 2008 and winter 2009) are used to examine also the seasonal dependence of the air quality - climate interactions. The results of these sensitivity simulations suggest that there is an important link between changes in meteorology and PM2.5 levels. We quantify through separate sensitivity simulations the processes which are mainly responsible for the final predicted changes in PM2.5 concentration and composition. The predicted PM2.5 response to those meteorology perturbations was found to be quite variable in space and time. These results suggest that, the changes in concentrations caused by changes in climate should be taken into account in long-term air quality planning. References Fountoukis C., Racherla P. N., Denier van der Gon H. A. C., Polymeneas P., Charalampidis P. E., Pilinis C., Wiedensohler A., Dall'Osto M., O'Dowd C., and S. N. Pandis: Evaluation of a three-dimensional chemical transport model (PMCAMx) in the European domain during the EUCAARI May 2008 campaign, Atmos. Chem. Phys., 11, 10331-10347, 2011. Intergovernmental Panel on Climate Change (IPCC), Fourth Assessment Report: Summary for Policymakers, 2007. Seinfeld, J. H., and Pandis, S. N.: Atmospheric chemistry and physics: From air pollution to climate change, 2nd ed.; John Wiley and Sons, Hoboken, NJ, 2006.

Intercomparison of planetary boundary layer parameterization and its impacts on surface ozone concentration in the WRF/Chem model for a case study in Houston/Texas

NASA Astrophysics Data System (ADS)

Cuchiara, G. C.; Li, X.; Carvalho, J.; Rappenglück, B.

2014-10-01

With over 6 million inhabitants the Houston metropolitan area is the fourth-largest in the United States. Ozone concentration in this southeast Texas region frequently exceeds the National Ambient Air Quality Standard (NAAQS). For this reason our study employed the Weather Research and Forecasting model with Chemistry (WRF/Chem) to quantify meteorological prediction differences produced by four widely used PBL schemes and analyzed its impact on ozone predictions. The model results were compared to observational data in order to identify one superior PBL scheme better suited for the area. The four PBL schemes include two first-order closure schemes, the Yonsei University (YSU) and the Asymmetric Convective Model version 2 (ACM2); as well as two turbulent kinetic energy closure schemes, the Mellor-Yamada-Janjic (MYJ) and Quasi-Normal Scale Elimination (QNSE). Four 24 h forecasts were performed, one for each PBL scheme. Simulated vertical profiles for temperature, potential temperature, relative humidity, water vapor mixing ratio, and the u-v components of the wind were compared to measurements collected during the Second Texas Air Quality Study (TexAQS-II) Radical and Aerosol Measurements Project (TRAMP) experiment in summer 2006. Simulated ozone was compared against TRAMP data, and air quality stations from Continuous Monitoring Station (CAMS). Also, the evolutions of the PBL height and vertical mixing properties within the PBL for the four simulations were explored. Although the results yielded high correlation coefficients and small biases in almost all meteorological variables, the overall results did not indicate any preferred PBL scheme for the Houston case. However, for ozone prediction the YSU scheme showed greatest agreements with observed values.

Intercomparison of Planetary Boundary Layer Parameterization and its Impacts on Surface Ozone Concentration in the WRF/Chem Model for a Case Study in Houston/Texas

NASA Astrophysics Data System (ADS)

Cuchiara, Gustavo C.; Li, Xiangshang; Carvalho, Jonas; Rappenglück, Bernhard

2015-04-01

With over 6 million inhabitants the Houston metropolitan area is the fourth-largest in the United States. Ozone concentration in this southeast Texas region frequently exceeds the National Ambient Air Quality Standard (NAAQS). For this reason our study employed the Weather Research and Forecasting model with Chemistry (WRF/Chem) to quantify meteorological prediction differences produced by four widely used PBL schemes and analyzed its impact on ozone predictions. The model results were compared to observational data in order to identify one superior PBL scheme better suited for the area. The four PBL schemes include two first-order closure schemes, the Yonsei University (YSU) and the Asymmetric Convective Model version 2 (ACM2); as well as two turbulent kinetic energy closure schemes, the Mellor-Yamada-Janjic (MYJ) and Quasi-Normal Scale Elimination (QNSE). Four 24 h forecasts were performed, one for each PBL scheme. Simulated vertical profiles for temperature, potential temperature, relative humidity, water vapor mixing ratio, and the u-v components of the wind were compared to measurements collected during the Second Texas Air Quality Study (TexAQS-II) Radical and Aerosol Measurements Project (TRAMP) experiment in summer 2006. Simulated ozone was compared against TRAMP data, and air quality stations from Continuous Monitoring Station (CAMS). Also, the evolutions of the PBL height and vertical mixing properties within the PBL for the four simulations were explored. Although the results yielded high correlation coefficients and small biases in almost all meteorological variables, the overall results did not indicate any preferred PBL scheme for the Houston case. However, for ozone prediction the YSU scheme showed greatest agreements with observed values.

Optimal Design of Air Quality Monitoring Network and its Application in an Oil Refinery Plant: An Approach to Keep Health Status of Workers.

PubMed

ZoroufchiBenis, Khaled; Fatehifar, Esmaeil; Ahmadi, Javad; Rouhi, Alireza

2015-01-01

Industrial air pollution is a growing challenge to humane health, especially in developing countries, where there is no systematic monitoring of air pollution. Given the importance of the availability of valid information on population exposure to air pollutants, it is important to design an optimal Air Quality Monitoring Network (AQMN) for assessing population exposure to air pollution and predicting the magnitude of the health risks to the population. A multi-pollutant method (implemented as a MATLAB program) was explored for configur-ing an AQMN to detect the highest level of pollution around an oil refinery plant. The method ranks potential monitoring sites (grids) according to their ability to represent the ambient concentration. The term of cluster of contiguous grids that exceed a threshold value was used to calculate the Station Dosage. Selection of the best configuration of AQMN was done based on the ratio of a sta-tion's dosage to the total dosage in the network. Six monitoring stations were needed to detect the pollutants concentrations around the study area for estimating the level and distribution of exposure in the population with total network efficiency of about 99%. An analysis of the design procedure showed that wind regimes have greatest effect on the location of monitoring stations. The optimal AQMN enables authorities to implement an effective program of air quality management for protecting human health.

Optimal Design of Air Quality Monitoring Network and its Application in an Oil Refinery Plant: An Approach to Keep Health Status of Workers

PubMed Central

ZoroufchiBenis, Khaled; Fatehifar, Esmaeil; Ahmadi, Javad; Rouhi, Alireza

2015-01-01

Background: Industrial air pollution is a growing challenge to humane health, especially in developing countries, where there is no systematic monitoring of air pollution. Given the importance of the availability of valid information on population exposure to air pollutants, it is important to design an optimal Air Quality Monitoring Network (AQMN) for assessing population exposure to air pollution and predicting the magnitude of the health risks to the population. Methods: A multi-pollutant method (implemented as a MATLAB program) was explored for configur­ing an AQMN to detect the highest level of pollution around an oil refinery plant. The method ranks potential monitoring sites (grids) according to their ability to represent the ambient concentration. The term of cluster of contiguous grids that exceed a threshold value was used to calculate the Station Dosage. Selection of the best configuration of AQMN was done based on the ratio of a sta­tion’s dosage to the total dosage in the network. Results: Six monitoring stations were needed to detect the pollutants concentrations around the study area for estimating the level and distribution of exposure in the population with total network efficiency of about 99%. An analysis of the design procedure showed that wind regimes have greatest effect on the location of monitoring stations. Conclusion: The optimal AQMN enables authorities to implement an effective program of air quality management for protecting human health. PMID:26933646

Evaluating the impact of ambient benzene vapor concentrations on product water from Condensation Water From Air technology.

PubMed

Kinder, Katherine M; Gellasch, Christopher A; Dusenbury, James S; Timmes, Thomas C; Hughes, Thomas M

2017-07-15

Globally, drinking water resources are diminishing in both quantity and quality. This situation has renewed interest in Condensation Water From Air (CWFA) technology, which utilizes water vapor in the air to produce water for both potable and non-potable purposes. However, there are currently insufficient data available to determine the relationship between air contaminants and the rate at which they are transferred from the air into CWFA untreated product water. This study implemented a novel experimental method utilizing an environmental test chamber to evaluate how air quality and temperature affects CWFA untreated product water quality in order to collect data that will inform the type of water treatment required to protect human health. This study found that temperature and benzene air concentration affected the untreated product water from a CWFA system. Benzene vapor concentrations representing a polluted outdoor environment resulted in benzene product water concentrations between 15% and 23% of the USEPA drinking water limit of 5μg/l. In contrast, product water benzene concentrations representing an indoor industrial environment were between 1.4 and 2.4 times higher than the drinking water limit. Lower condenser coil temperatures were correlated with an increased concentration of benzene in the product water. Environmental health professionals and engineers can integrate the results of this assessment to predict benzene concentrations in the product water and take appropriate health protective measures. Published by Elsevier B.V.

Application and evaluation of two air quality models for particulate matter for a southeastern U.S. episode.

PubMed

Zhang, Yang; Pun, Betty; Wu, Shiang-Yuh; Vijayaraghavan, Krish; Seigneur, Christian

2004-12-01

The Models-3 Community Multiscale Air Quality (CMAQ) Modeling System and the Particulate Matter Comprehensive Air Quality Model with extensions (PMCAMx) were applied to simulate the period June 29-July 10, 1999, of the Southern Oxidants Study episode with two nested horizontal grid sizes: a coarse resolution of 32 km and a fine resolution of 8 km. The predicted spatial variations of ozone (O3), particulate matter with an aerodynamic diameter less than or equal to 2.5 microm (PM2.5), and particulate matter with an aerodynamic diameter less than or equal to 10 microm (PM10) by both models are similar in rural areas but differ from one another significantly over some urban/suburban areas in the eastern and southern United States, where PMCAMx tends to predict higher values of O3 and PM than CMAQ. Both models tend to predict O3 values that are higher than those observed. For observed O3 values above 60 ppb, O3 performance meets the U.S. Environmental Protection Agency's criteria for CMAQ with both grids and for PMCAMx with the fine grid only. It becomes unsatisfactory for PMCAMx and marginally satisfactory for CMAQ for observed O3 values above 40 ppb. Both models predict similar amounts of sulfate (SO4(2-)) and organic matter, and both predict SO4(2-) to be the largest contributor to PM2.5. PMCAMx generally predicts higher amounts of ammonium (NH4+), nitrate (NO3-), and black carbon (BC) than does CMAQ. PM performance for CMAQ is generally consistent with that of other PM models, whereas PMCAMx predicts higher concentrations of NO3-, NH4+, and BC than observed, which degrades its performance. For PM10 and PM2.5 predictions over the southeastern U.S. domain, the ranges of mean normalized gross errors (MNGEs) and mean normalized bias are 37-43% and -33-4% for CMAQ and 50-59% and 7-30% for PMCAMx. Both models predict the largest MNGEs for NO3- (98-104% for CMAQ 138-338% for PMCAMx). The inaccurate NO3- predictions by both models may be caused by the inaccuracies in the ammonia emission inventory and the uncertainties in the gas/particle partitioning under some conditions. In addition to these uncertainties, the significant PM overpredictions by PMCAMx may be attributed to the lack of wet removal for PM and a likely underprediction in the vertical mixing during the daytime.

Entrainment of stratospheric air and Asian pollution by the convective boundary layer in the southwestern U.S.

NASA Astrophysics Data System (ADS)

Langford, A. O.; Alvarez, R. J.; Brioude, J.; Fine, R.; Gustin, M. S.; Lin, M. Y.; Marchbanks, R. D.; Pierce, R. B.; Sandberg, S. P.; Senff, C. J.; Weickmann, A. M.; Williams, E. J.

2017-01-01

A series of deep stratospheric intrusions in late May 2013 increased the daily maximum 8 h surface ozone (O3) concentrations to more than 70 parts per billion by volume (ppbv) at rural and urban surface monitors in California and Nevada. This influx of ozone-rich lower stratospheric air and entrained Asian pollution persisted for more than 5 days and contributed to exceedances of the 2008 8 h national ambient air quality standard of 75 ppbv on 21 and 25 May in Clark County, NV. Exceedances would also have occurred on 22 and 23 May had the new standard of 70 ppbv been in effect. In this paper, we examine this episode using lidar measurements from a high-elevation site on Angel Peak, NV, and surface measurements from NOAA, the Clark County, Nevada Department of Air Quality, the Environmental Protection Agency Air Quality System, and the Nevada Rural Ozone Initiative. These measurements, together with analyses from the National Centers for Environmental Prediction/North American Regional Reanalysis; NOAA Geophysical Fluid Dynamics Laboratory AM3 model; NOAA National Environmental Satellite, Data, and Information Service Real-time Air Quality Modeling System; and FLEXPART models, show that the exceedances followed entrainment of 20 to 40 ppbv of lower stratospheric ozone mingled with another 0 to 10 ppbv of ozone transported from Asia by the unusually deep convective boundary layers above the Mojave desert. Our analysis suggests that this vigorous mixing can affect both high and low elevations and help explain the springtime ozone maximum in the southwestern U.S.

Stratospheric Instrusion Catalog: A 10-Year Compilation of Events Identified by using TRACK with NASA's MERRA-2 Reanalysis

NASA Technical Reports Server (NTRS)

Knowland, K. Emma; Ott, Lesley E.; Duncan, Bryan N.; Wargan, Kris; Hodges, Kevin

2017-01-01

Stratospheric intrusions "the introduction of ozone-rich stratospheric air into the troposphere" have been linked with surface ozone air quality exceedances, especially at the high elevations in the western USA in springtime. However, the impact of stratospheric intrusions in the remaining seasons and over the rest of the USA is less clear. A new approach to the study of stratospheric intrusions uses NASA's Goddard Earth Observing System Model (GEOS) model and assimilation products with an objective feature tracking algorithm to investigate the atmospheric dynamics that generate stratospheric intrusions and the different mechanisms through which stratospheric intrusions may influence tropospheric chemistry and surface air quality seasonally over both the western and the eastern USA. A catalog of stratospheric intrusions identified in the MERRA-2 reanalysis was produced for the period 2004-2015 and validated against surface ozone observations (focusing on those which exceed the national air quality standard) and a recent data set of stratospheric intrusion-influenced air quality exceedance flags from the US Environmental Protection Agency (EPA). Considering not all ozone exceedances have been flagged by the EPA, a collection of stratospheric intrusions can support air quality agencies for more rapid identification of the impact of stratospheric air on surface ozone and demonstrates that future operational analyses may aid in forecasting such events. An analysis of the spatiotemporal variability of stratospheric intrusions over the continental US was performed, and while the spring over the western USA does exhibit the largest number of stratospheric intrusions affecting the lower troposphere, the number of intrusions in the remaining seasons and over the eastern USA is sizable. By focusing on the major modes of variability that influence weather in the USA, such as the Pacific North American (PNA) teleconnection index, predicative meteorological patterns associated with stratospheric intrusions and their regional effects on tropospheric ozone were identified. Improved understanding of the connections between large-scale climate variability and local-scale dynamically-driven air quality events may support improved seasonal prediction of such events.

Stratospheric Intrusion Catalog: A 10-year Compilation of Events Identified By Using an Objective Feature Tracking Model With NASA's MERRA-2 Reanalysis

NASA Astrophysics Data System (ADS)

Knowland, K. E.; Ott, L. E.; Duncan, B. N.; Wargan, K.; Hodges, K.

2017-12-01

Stratospheric intrusions - the introduction of ozone-rich stratospheric air into the troposphere - have been linked with surface ozone air quality exceedances, especially at the high elevations in the western USA in springtime. However, the impact of stratospheric intrusions in the remaining seasons and over the rest of the USA is less clear. A new approach to the study of stratospheric intrusions uses NASA's Goddard Earth Observing System Model (GEOS) model and assimilation products with an objective feature tracking algorithm to investigate the atmospheric dynamics that generate stratospheric intrusions and the different mechanisms through which stratospheric intrusions may influence tropospheric chemistry and surface air quality seasonally over both the western and the eastern USA. A catalog of stratospheric intrusions identified in the MERRA-2 reanalysis was produced for the period 2005-2014 and validated against surface ozone observations (focusing on those which exceed the national air quality standard) and a recent data set of stratospheric intrusion-influenced air quality exceedance flags from the US Environmental Protection Agency (EPA). Considering not all ozone exceedances have been flagged by the EPA, a collection of stratospheric intrusions can support air quality agencies for more rapid identification of the impact of stratospheric air on surface ozone and demonstrates that future operational analyses may aid in forecasting such events. An analysis of the spatiotemporal variability of stratospheric intrusions over the continental US was performed, and while the spring over the western USA does exhibit the largest number of stratospheric intrusions affecting the lower troposphere, the number of intrusions in the remaining seasons and over the eastern USA is sizable. By focusing on the major modes of variability that influence weather in the USA, such as the Pacific North American (PNA) teleconnection index, predicative meteorological patterns associated with stratospheric intrusions and their regional effects on tropospheric ozone were identified. Improved understanding of the connections between large-scale climate variability and local-scale dynamically-driven air quality events may support improved seasonal prediction of such events.

Forecasting daily attendances at an emergency department to aid resource planning

PubMed Central

Sun, Yan; Heng, Bee Hoon; Seow, Yian Tay; Seow, Eillyne

2009-01-01

Background Accurate forecasting of emergency department (ED) attendances can be a valuable tool for micro and macro level planning. Methods Data for analysis was the counts of daily patient attendances at the ED of an acute care regional general hospital from July 2005 to Mar 2008. Patients were stratified into three acuity categories; i.e. P1, P2 and P3, with P1 being the most acute and P3 being the least acute. The autoregressive integrated moving average (ARIMA) method was separately applied to each of the three acuity categories and total patient attendances. Independent variables included in the model were public holiday (yes or no), ambient air quality measured by pollution standard index (PSI), daily ambient average temperature and daily relative humidity. The seasonal components of weekly and yearly periodicities in the time series of daily attendances were also studied. Univariate analysis by t-tests and multivariate time series analysis were carried out in SPSS version 15. Results By time series analyses, P1 attendances did not show any weekly or yearly periodicity and was only predicted by ambient air quality of PSI > 50. P2 and total attendances showed weekly periodicities, and were also significantly predicted by public holiday. P3 attendances were significantly correlated with day of the week, month of the year, public holiday, and ambient air quality of PSI > 50. After applying the developed models to validate the forecast, the MAPE of prediction by the models were 16.8%, 6.7%, 8.6% and 4.8% for P1, P2, P3 and total attendances, respectively. The models were able to account for most of the significant autocorrelations present in the data. Conclusion Time series analysis has been shown to provide a useful, readily available tool for predicting emergency department workload that can be used to plan staff roster and resource planning. PMID:19178716

Predicting PM10 concentration in Seoul metropolitan subway stations using artificial neural network (ANN).

PubMed

Park, Sechan; Kim, Minjeong; Kim, Minhae; Namgung, Hyeong-Gyu; Kim, Ki-Tae; Cho, Kyung Hwa; Kwon, Soon-Bark

2018-01-05

The indoor air quality of subway systems can significantly affect the health of passengers since these systems are widely used for short-distance transit in metropolitan urban areas in many countries. The particles generated by abrasion during subway operations and the vehicle-emitted pollutants flowing in from the street in particular affect the air quality in underground subway stations. Thus the continuous monitoring of particulate matter (PM) in underground station is important to evaluate the exposure level of PM to passengers. However, it is difficult to obtain indoor PM data because the measurement systems are expensive and difficult to install and operate for significant periods of time in spaces crowded with people. In this study, we predicted the indoor PM concentration using the information of outdoor PM, the number of subway trains running, and information on ventilation operation by the artificial neural network (ANN) model. As well, we investigated the relationship between ANN's performance and the depth of underground subway station. ANN model showed a high correlation between the predicted and actual measured values and it was able to predict 67∼80% of PM at 6 subway station. In addition, we found that platform shape and depth influenced the model performance. Copyright © 2017 Elsevier B.V. All rights reserved.

Air Quality and Heart Health: An Emerging Topic for Heart ...

EPA Pesticide Factsheets

Air Quality and Heart Health: An Emerging Topic for Heart Month: Ambient air particle pollution increases short- and long-term cardiovascular morbidity and mortality. Older-people, those with pre-existing heart disease and lung disease and diabetes are at higher risk. Mechanisms are under investigation and are likely related to oxidative stress, inflammation and effects on autonomic control. Improvements in air pollution levels reduce health impacts and increase life expectancy. Reductions of short-term exposure in those at highest risk are predicted to mitigate adverse health effects. EPA regularly evaluates the standards, health risks and issues improved standards when needed. Public health action is needed along with EPA standards to reduce the public health burden of short- and long-term adverse health effects of air pollution. Health risks remain and need to be addressed through integrated efforts of public health, health care, environmental health, individuals and communities. Presented at Webinar for the National Association of Clean Air Agencies, February 2, 2017, Chapel Hill, NC- This webinar provided an update of environmental health information related to the effects of air pollution and heart and blood vessel disease. Such information is critically important for the Clean Air Agencies to understand as it provides the justification of their actions.

Statistical Analysis of the Impacts of Regional Transportation on the Air Quality in Beijing

NASA Astrophysics Data System (ADS)

Huang, Zhongwen; Zhang, Huiling; Tong, Lei; Xiao, Hang

2016-04-01

From October to December 2015, Beijing-Tianjin-Hebei (BTH) region had experienced several severe haze events. In order to assess the effects of the regional transportation on the air quality in Beijing, the air monitoring data (PM2.5, SO2, NO2 and CO) from that period published by Chinese National Environmental Monitoring Center (CNEMC) was collected and analyzed with various statistical models. The cities within BTH area were clustered into three groups according to the geographical conditions, while the air pollutant concentrations of cities within a group sharing similar variation trends. The Granger causality test results indicate that significant causal relationships exist between the air pollutant data of Beijing and its surrounding cities (Baoding, Chengde, Tianjin and Zhangjiakou) for the reference period. Then, linear regression models were constructed to capture the interdependency among the multiple time series. It shows that the observed air pollutant concentrations in Beijing were well consistent with the model-fitted results. More importantly, further analysis suggests that the air pollutants in Beijing were strongly affected by regional transportation, as the local sources only contributed 17.88%, 27.12%, 14.63% and 31.36% of PM2.5, SO2, NO2 and CO concentrations, respectively. And the major foreign source for Beijing was from Southwest (Baoding) direction, account for more than 42% of all these air pollutants. Thus, by combining various statistical models, it may not only be able to quickly predict the air qualities of any cities on a regional scale, but also to evaluate the local and regional source contributions for a particular city. Key words: regional transportation, air pollution, Granger causality test, statistical models

Saturday Driving Restrictions Fail to Improve Air Quality in Mexico City.

PubMed

Davis, Lucas W

2017-02-02

Policymakers around the world are turning to license-plate based driving restrictions in an effort to address urban air pollution. The format differs across cities, but most programs restrict driving once or twice a week during weekdays. This paper focuses on Mexico City, home to one of the oldest and best-known driving restriction policies. For almost two decades Mexico City's driving restrictions applied during weekdays only. This changed recently, however, when the program was expanded to include Saturdays. This paper uses hourly data from pollution monitoring stations to measure the effect of the Saturday expansion on air quality. Overall, there is little evidence that the program expansion improved air quality. Across eight major pollutants, the program expansion had virtually no discernible effect on pollution levels. These disappointing results stand in sharp contrast to estimates made before the expansion which predicted a 15%+ decrease in vehicle emissions on Saturdays. To understand why the program has been less effective than expected, the paper then turns to evidence from subway, bus, and light rail ridership, finding no evidence that the expansion was successful in getting drivers to switch to lower-emitting forms of transportation.

Saturday Driving Restrictions Fail to Improve Air Quality in Mexico City

NASA Astrophysics Data System (ADS)

Davis, Lucas W.

2017-02-01

Policymakers around the world are turning to license-plate based driving restrictions in an effort to address urban air pollution. The format differs across cities, but most programs restrict driving once or twice a week during weekdays. This paper focuses on Mexico City, home to one of the oldest and best-known driving restriction policies. For almost two decades Mexico City’s driving restrictions applied during weekdays only. This changed recently, however, when the program was expanded to include Saturdays. This paper uses hourly data from pollution monitoring stations to measure the effect of the Saturday expansion on air quality. Overall, there is little evidence that the program expansion improved air quality. Across eight major pollutants, the program expansion had virtually no discernible effect on pollution levels. These disappointing results stand in sharp contrast to estimates made before the expansion which predicted a 15%+ decrease in vehicle emissions on Saturdays. To understand why the program has been less effective than expected, the paper then turns to evidence from subway, bus, and light rail ridership, finding no evidence that the expansion was successful in getting drivers to switch to lower-emitting forms of transportation.

Saturday Driving Restrictions Fail to Improve Air Quality in Mexico City

PubMed Central

Davis, Lucas W.

2017-01-01

Policymakers around the world are turning to license-plate based driving restrictions in an effort to address urban air pollution. The format differs across cities, but most programs restrict driving once or twice a week during weekdays. This paper focuses on Mexico City, home to one of the oldest and best-known driving restriction policies. For almost two decades Mexico City’s driving restrictions applied during weekdays only. This changed recently, however, when the program was expanded to include Saturdays. This paper uses hourly data from pollution monitoring stations to measure the effect of the Saturday expansion on air quality. Overall, there is little evidence that the program expansion improved air quality. Across eight major pollutants, the program expansion had virtually no discernible effect on pollution levels. These disappointing results stand in sharp contrast to estimates made before the expansion which predicted a 15%+ decrease in vehicle emissions on Saturdays. To understand why the program has been less effective than expected, the paper then turns to evidence from subway, bus, and light rail ridership, finding no evidence that the expansion was successful in getting drivers to switch to lower-emitting forms of transportation. PMID:28151487

Effect of aerosol feedback in the Korea Peninsula using WRF-CMAQ two-way coupled model

NASA Astrophysics Data System (ADS)

Yoo, J.; Jeon, W.; Lee, H.; Lee, S.

2017-12-01

Aerosols influence the climate system by scattering and absorption of the solar radiation by altering the cloud radiative properties. For the reason, consideration of aerosol feedback is important numerical weather prediction and air quality models. The purpose of this study was to investigate the effect of aerosol feedback on PM10 simulation in Korean Peninsula using the Weather Research and Forecasting (WRF) and the community multiscale air quality (CMAQ) two-way coupled model. Simulations were conducted with the aerosol feedback (FB) and without (NFB). The results of the simulated solar radiation in the west part of Korea decreased due to the aerosol feedback effect. The feedback effect was significant in the west part of Korea Peninsula, showing high Particulate Matter (PM) estimates due to dense emissions and its long-range transport from China. The decrease of solar radiation lead to planetary boundary layer (PBL) height reduction, thereby dispersion of air pollutants such as PM is suppressed, and resulted in higher PM concentrations. These results indicate that aerosol feedback effects can play an important role in the simulation of meteorology and air quality over Korea Peninsula.

Experimental study and empirical prediction of fuel flow parameters under air evolution conditions

NASA Astrophysics Data System (ADS)

Kitanina, E. E.; Kitanin, E. L.; Bondarenko, D. A.; Kravtsov, P. A.; Peganova, M. M.; Stepanov, S. G.; Zherebzov, V. L.

2017-11-01

Air evolution in kerosene under the effect of gravity flow with various hydraulic resistances in the pipeline was studied experimentally. The study was conducted at pressure ranging from 0.2 to 1.0 bar and temperature varying between -20°C and +20°C. Through these experiments, the oversaturation limit beyond which dissolved air starts evolving intensively from the fuel was established and the correlations for the calculation of pressure losses and air evolution on local loss elements were obtained. A method of calculating two-phase flow behaviour in a titled pipeline segment with very low mass flow quality and fairly high volume flow quality was developed. The complete set of empirical correlations obtained by experimental analysis was implemented in the engineering code. The software simulation results were repeatedly verified against our experimental findings and Airbus test data to show that the two-phase flow simulation agrees quite well with the experimental results obtained in the complex branched pipelines.

Enhancing wind erosion monitoring and assessment for U.S. rangelands

USGS Publications Warehouse

Webb, Nicholas P.; Van Zee, Justin W.; Karl, Jason W.; Herrick, Jeffrey E.; Courtright, Ericha M.; Billings, Benjamin J.; Boyd, Robert C.; Chappell, Adrian; Duniway, Michael C.; Derner, Justin D.; Hand, Jenny L.; Kachergis, Emily; McCord, Sarah E.; Newingham, Beth A.; Pierson, Frederick B.; Steiner, Jean L.; Tatarko, John; Tedela, Negussie H.; Toledo, David; Van Pelt, R. Scott

2017-01-01

On the GroundWind erosion is a major resource concern for rangeland managers because it can impact soil health, ecosystem structure and function, hydrologic processes, agricultural production, and air quality.Despite its significance, little is known about which landscapes are eroding, by how much, and when.The National Wind Erosion Research Network was established in 2014 to develop tools for monitoring and assessing wind erosion and dust emissions across the United States.The Network, currently consisting of 13 sites, creates opportunities to enhance existing rangeland soil, vegetation, and air quality monitoring programs.Decision-support tools developed by the Network will improve the prediction and management of wind erosion across rangeland ecosystems.

Assesment of PM10 pollution episodes in a ceramic cluster (NE Spain): proposal of a new quality index for PM10, As, Cd, Ni and Pb.

PubMed

Vicente, A B; Sanfeliu, T; Jordan, M M

2012-10-15

Environmental pollution control is one of the most important goals in pollution risk assessment today. In this sense, modern and precise tools that allow scientists to evaluate, quantify and predict air pollution are of particular interest. Monitoring atmospheric particulate matter is a challenge faced by the European Union. Specific rules on this subject are being developed (Directive 2004/107/EC, Directive 2008/50/EC) in order to reduce the potential adverse effects on human health caused by air pollution. Air pollution has two sources: natural and anthropogenic. Contributions from natural sources can be assessed but cannot be controlled, while emissions from anthropogenic sources can be controlled; monitoring to reduce this latter type of pollution should therefore be carried out. In this paper, we describe an air quality evaluation in terms of levels of atmospheric particles (PM10), as outlined by European Union legislation, carried out in an industrialised Spanish coastal area over a five-year period with the purpose of comparing these values with those of other areas in the Mediterranean Basin with different weather conditions from North of Europe. The study area is in the province of Castellón. This province is a strategic area in the frame work of European Union (EU) pollution control. Approximately 80% of European ceramic tiles and ceramic frit manufacturers are concentrated in two areas, forming the so-called "ceramics clusters"; ones in Modena (Italy) and the other in Castellón. In this kind of areas, there are a lot of air pollutants from this industry then it is difficult to fulfill de European limits of PM10 so it is necessary to control the air quality in them. The seasonal differences in the number of days in which pollutant level limits were exceeded were evaluated and the sources of contamination were identified. Air quality indexes for each pollutant have been established to determine easily and clearly the quality of air breathed. Furthermore, in accordance with Directive 2008/50/EC, an Air Quality Plan is proposed to protect human health, and the environment as a whole, in the study area. General and specific corrective measures of main emission sources are provided. A strategy for air pollution management is thus presented. Copyright © 2012 Elsevier Ltd. All rights reserved.

Artificial neural network model for ozone concentration estimation and Monte Carlo analysis

NASA Astrophysics Data System (ADS)

Gao, Meng; Yin, Liting; Ning, Jicai

2018-07-01

Air pollution in urban atmosphere directly affects public-health; therefore, it is very essential to predict air pollutant concentrations. Air quality is a complex function of emissions, meteorology and topography, and artificial neural networks (ANNs) provide a sound framework for relating these variables. In this study, we investigated the feasibility of using ANN model with meteorological parameters as input variables to predict ozone concentration in the urban area of Jinan, a metropolis in Northern China. We firstly found that the architecture of network of neurons had little effect on the predicting capability of ANN model. A parsimonious ANN model with 6 routinely monitored meteorological parameters and one temporal covariate (the category of day, i.e. working day, legal holiday and regular weekend) as input variables was identified, where the 7 input variables were selected following the forward selection procedure. Compared with the benchmarking ANN model with 9 meteorological and photochemical parameters as input variables, the predicting capability of the parsimonious ANN model was acceptable. Its predicting capability was also verified in term of warming success ratio during the pollution episodes. Finally, uncertainty and sensitivity analysis were also performed based on Monte Carlo simulations (MCS). It was concluded that the ANN could properly predict the ambient ozone level. Maximum temperature, atmospheric pressure, sunshine duration and maximum wind speed were identified as the predominate input variables significantly influencing the prediction of ambient ozone concentrations.

Refinement and testing of analysis nudging in MPAS-A

EPA Science Inventory

The Model for Prediction Across Scales - Atmosphere (MPAS-A) is being adapted to serve as the meteorological driver for EPA’s “next-generation” air-quality model. To serve that purpose, it must be able to function in a diagnostic mode where past meteorological ...

NEW CATEGORICAL METRICS FOR AIR QUALITY MODEL EVALUATION

EPA Science Inventory

Traditional categorical metrics used in model evaluations are "clear-cut" measures in that the model's ability to predict an exceedance is defined by a fixed threshold concentration and the metrics are defined by observation-forecast sets that are paired both in space and time. T...

The role of future scenarios to understand deep uncertainty in air quality management

EPA Science Inventory

The environment and it’s interaction with human systems (economic, social and political) is complex and dynamic. Key drivers may disrupt system dynamics in unforeseen ways, making it difficult to predict future conditions precisely. This kind of deep uncertainty presents ...

The role of future scenarios to understand deep uncertainty for air quality management.

EPA Science Inventory

The environment and its interaction with human systems (economic, social and political) is complex and dynamic. Key drivers may disrupt systemdynamics in unforeseen ways, making it difficult to predict future conditions precisely. This kind of deep uncertainty presents a challeng...

COMPARISON OF SPATIAL PATTERNS OF POLLUTANT DISTRIBUTION WITH CMAQ PREDICTIONS

EPA Science Inventory

To evaluate the Models-3/Community Multiscale Air Quality (CMAQ) modeling system in reproducing the spatial patterns of aerosol concentrations over the country on timescales of months and years, the spatial patterns of model output are compared with those derived from observation...

THE EFFECTS OF ROADSIDE STRUCTURES ON THE TRANSPORT AND DISPERSION OF ULTRAFINE PARTICLES FROM HIGHWAYS.

EPA Science Inventory

Understanding the transport and dispersion of pollutants from traffic sources, particularly within 300 meters of a roadway is important both for urban planning and for air quality assessments. Predicting pollutant concentration patterns in complex environments depends on accurat...

Predicting SOA from organic nitrates in the southeastern United States

EPA Science Inventory

Organic nitrates have been identified as an important component of ambient aerosol in the Southeast United States. In this work, we use the Community Multiscale Air Quality (CMAQ) model to explore the relationship between gas-phase production of organic nitrates and their subsequ...

Public perception of rural environmental quality: Moving towards a multi-pollutant approach

NASA Astrophysics Data System (ADS)

Cantuaria, Manuella Lech; Brandt, Jørgen; Løfstrøm, Per; Blanes-Vidal, Victoria

2017-12-01

Most environmental epidemiology studies have examined pollutants individually. Multi-pollutant approaches have been recognized recently, but to the extent of our knowledge, no study to date has specifically investigated exposures to multiple air pollutants in rural environments. In this paper we characterized and quantified residential exposures to air pollutant mixtures in rural populations, provided a better understanding of the relationships between air pollutant mixtures and annoyance responses to environmental stressors, particularly odor, and quantified their predictive abilities. We used validated and highly spatially resolved atmospheric modeling of 14 air pollutants for four rural areas of Denmark, and the annoyance responses considered were annoyance due to odor, noise, dust, smoke and vibrations. We found significant associations between odor annoyance and principal components predominantly described by nitrate (NO3-), ammonium (NH4+), particulate matter (PM10 and PM2.5) and NH3, which are usually related to agricultural emission sources. Among these components, NH3 showed the lowest error when comparing observed population data and predicted probabilities. The combination of these compounds in a predictive model resulted in the most accurate model, being able to correctly predict 66% of odor annoyance responses. Furthermore, noise annoyance was found to be significantly associated with traffic-related air pollutants. In general terms, our results suggest that emissions from the agricultural and livestock production sectors are the main contributors to environmental annoyance, but also identify traffic and biomass burning as potential sources of annoyance.

Evaluating the Impact of Aerosols on Numerical Weather Prediction

NASA Astrophysics Data System (ADS)

Freitas, Saulo; Silva, Arlindo; Benedetti, Angela; Grell, Georg; Members, Wgne; Zarzur, Mauricio

2015-04-01

The Working Group on Numerical Experimentation (WMO, http://www.wmo.int/pages/about/sec/rescrosscut/resdept_wgne.html) has organized an exercise to evaluate the impact of aerosols on NWP. This exercise will involve regional and global models currently used for weather forecast by the operational centers worldwide and aims at addressing the following questions: a) How important are aerosols for predicting the physical system (NWP, seasonal, climate) as distinct from predicting the aerosols themselves? b) How important is atmospheric model quality for air quality forecasting? c) What are the current capabilities of NWP models to simulate aerosol impacts on weather prediction? Toward this goal we have selected 3 strong or persistent events of aerosol pollution worldwide that could be fairly represented in current NWP models and that allowed for an evaluation of the aerosol impact on weather prediction. The selected events includes a strong dust storm that blew off the coast of Libya and over the Mediterranean, an extremely severe episode of air pollution in Beijing and surrounding areas, and an extreme case of biomass burning smoke in Brazil. The experimental design calls for simulations with and without explicitly accounting for aerosol feedbacks in the cloud and radiation parameterizations. In this presentation we will summarize the results of this study focusing on the evaluation of model performance in terms of its ability to faithfully simulate aerosol optical depth, and the assessment of the aerosol impact on the predictions of near surface wind, temperature, humidity, rainfall and the surface energy budget.

Predicting Envelope Leakage in Attached Dwellings

DOE Office of Scientific and Technical Information (OSTI.GOV)

Faakye, O.; Arena, L.; Griffiths, D.

2013-07-01

The most common method for measuring air leakage is to use a single blower door to pressurize and/or depressurize the test unit. In detached housing, the test unit is the entire home and the single blower door measures air leakage to the outside. In attached housing, this 'single unit', 'total', or 'solo' test method measures both the air leakage between adjacent units through common surfaces as well air leakage to the outside. Measuring and minimizing this total leakage is recommended to avoid indoor air quality issues between units, reduce energy losses to the outside, reduce pressure differentials between units, andmore » control stack effect. However, two significant limitations of the total leakage measurement in attached housing are: for retrofit work, if total leakage is assumed to be all to the outside, the energy benefits of air sealing can be significantly over predicted; for new construction, the total leakage values may result in failing to meet an energy-based house tightness program criterion. The scope of this research is to investigate an approach for developing a viable simplified algorithm that can be used by contractors to assess energy efficiency program qualification and/or compliance based upon solo test results.« less

Using statistical models to explore ensemble uncertainty in climate impact studies: the example of air pollution in Europe

NASA Astrophysics Data System (ADS)

Lemaire, Vincent E. P.; Colette, Augustin; Menut, Laurent

2016-03-01

Because of its sensitivity to unfavorable weather patterns, air pollution is sensitive to climate change so that, in the future, a climate penalty could jeopardize the expected efficiency of air pollution mitigation measures. A common method to assess the impact of climate on air quality consists in implementing chemistry-transport models forced by climate projections. However, the computing cost of such methods requires optimizing ensemble exploration techniques. By using a training data set from a deterministic projection of climate and air quality over Europe, we identified the main meteorological drivers of air quality for eight regions in Europe and developed statistical models that could be used to predict air pollutant concentrations. The evolution of the key climate variables driving either particulate or gaseous pollution allows selecting the members of the EuroCordex ensemble of regional climate projections that should be used in priority for future air quality projections (CanESM2/RCA4; CNRM-CM5-LR/RCA4 and CSIRO-Mk3-6-0/RCA4 and MPI-ESM-LR/CCLM following the EuroCordex terminology). After having tested the validity of the statistical model in predictive mode, we can provide ranges of uncertainty attributed to the spread of the regional climate projection ensemble by the end of the century (2071-2100) for the RCP8.5. In the three regions where the statistical model of the impact of climate change on PM2.5 offers satisfactory performances, we find a climate benefit (a decrease of PM2.5 concentrations under future climate) of -1.08 (±0.21), -1.03 (±0.32), -0.83 (±0.14) µg m-3, for respectively Eastern Europe, Mid-Europe and Northern Italy. In the British-Irish Isles, Scandinavia, France, the Iberian Peninsula and the Mediterranean, the statistical model is not considered skillful enough to draw any conclusion for PM2.5. In Eastern Europe, France, the Iberian Peninsula, Mid-Europe and Northern Italy, the statistical model of the impact of climate change on ozone was considered satisfactory and it confirms the climate penalty bearing upon ozone of 10.51 (±3.06), 11.70 (±3.63), 11.53 (±1.55), 9.86 (±4.41), 4.82 (±1.79) µg m-3, respectively. In the British-Irish Isles, Scandinavia and the Mediterranean, the skill of the statistical model was not considered robust enough to draw any conclusion for ozone pollution.

Comparative evaluation of the impact of GRAPES and MM5 meteorology on CMAQ prediction over Pearl River Delta, China

NASA Astrophysics Data System (ADS)

Deng, T.; Chen, Y.; Wan, Q.

2017-12-01

The Community Multiscale Air Quality (CMAQ) model was utilized for forecasting air quality over the Pearl River Delta (PRD) region from December 2013 to January 2014. The pollution forecasting performance of CMAQ coupled with the two different meteorological models, the Global/Regional Assimilation and Prediction System (GRAPES) and the 5th-generation Mesoscale Model (MM5), was assessed by combining observational data. The effect of meteorological factors and physical-chemical processes on forecast results was discussed through process analysis. The results showed that both models have similar good performance with better performance by GRAPES-CMAQ. GRAPES was superior in predicting the overall meteorological element variation tendencies but showed large deviations in atmospheric pressure and wind speed. It contributed to higher correlation coefficients of the pollutants with GRAPES-CMAQ, but with greater deviation. The underestimations of nitrate and ammonium salt contributed to the underestimations of Particle Matter (PM) and extinction coefficients. Surface layer SO2, CO and NO source emissions made the sole positive contribution. O3 originated mainly from horizontal and vertical transport and chemical processes were the main consumption item. On the contrary, NO2 derived mainly from chemical production.

Projections of Future Summertime Ozone over the U.S.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Pfister, G. G.; Walters, Stacy; Lamarque, J. F.

This study uses a regional fully coupled chemistry-transport model to assess changes in surface ozone over the summertime U.S. between present and a 2050 future time period at high spatial resolution (12 km grid spacing) under the SRES A2 climate and RCP8.5 anthropogenic pre-cursor emission scenario. The impact of predicted changes in climate and global background ozone is estimated to increase surface ozone over most of the U.S; the 5th - 95th percentile range for daily 8-hour maximum surface ozone increases from 31-79 ppbV to 30-87 ppbV between the present and future time periods. The analysis of a set ofmore » meteorological drivers suggests that these mostly will add to increasing ozone, but the set of simulations conducted does not allow to separate this effect from that through enhanced global background ozone. Statistically the most robust positive feedbacks are through increased temperature, biogenic emissions and solar radiation. Stringent emission controls can counteract these feedbacks and if considered, we estimate large reductions in surface ozone with the 5th-95th percentile reduced to 27-55 ppbV. A comparison of the high-resolution projections to global model projections shows that even though the global model is biased high in surface ozone compared to the regional model and compared to observations, both the global and the regional model predict similar changes in ozone between the present and future time periods. However, on smaller spatial scales, the regional predictions show more pronounced changes between urban and rural regimes that cannot be resolved at the coarse resolution of global model. In addition, the sign of the changes in overall ozone mixing ratios can be different between the global and the regional predictions in certain regions, such as the Western U.S. This study confirms the key role of emission control strategies in future air quality predictions and demonstrates the need for considering degradation of air quality with future climate change in emission policy making. It also illustrates the need for high resolution modeling when the objective is to address regional and local air quality or establish links to human health and society.« less

The Influence of Fuelbed Physical Properties on Biomass Burning Emissions

NASA Astrophysics Data System (ADS)

Urbanski, S. P.; Lincoln, E.; Baker, S. P.; Richardson, M.

2014-12-01

Emissions from biomass fires can significantly degrade regional air quality and therefore are of major concern to air regulators and land managers in the U.S. and Canada. Accurately estimating emissions from different fire types in various ecosystems is crucial to predicting and mitigating the impact of fires on air quality. The physical properties of ecosystems' fuelbeds can heavily influence the combustion processes (e.g. flaming or smoldering) and the resultant emissions. However, despite recent progress in characterizing the composition of biomass smoke, significant knowledge gaps remain regarding the linkage between basic fuelbed physical properties and emissions. In laboratory experiments we examined the effects of fuelbed properties on combustion efficiency (CE) and emissions for an important fuel component of temperate and boreal forests - conifer needles. The bulk density (BD), depth (DZ), and moisture content (MC) of Ponderosa Pine needle fuelbeds were manipulated in 75 burns for which gas and particle emissions were measured. We found CE was negatively correlated with BD, DZ and MC and that the emission factors of species associated with smoldering combustion processes (CO, CH4, particles) were positively correlated with these fuelbed properties. The study indicates the physical properties of conifer needle fuelbeds have a significant effect on CE and hence emissions. However, many of the emission models used to predict and manage smoke impacts on air quality assume conifer litter burns by flaming combustion with a high CE and correspondingly low emissions of CO, CH4, particles, and organic compounds. Our results suggest emission models underestimate emissions from fires involving a large component of conifer needles. Additionally, our findings indicate that laboratory studies of emissions should carefully control fuelbed physical properties to avoid confounding effects that may obscure the effects being tested and lead to erroneous interpretations.

Modeling multi-scale aerosol dynamics and micro-environmental air quality near a large highway intersection using the CTAG model.

PubMed

Wang, Yan Jason; Nguyen, Monica T; Steffens, Jonathan T; Tong, Zheming; Wang, Yungang; Hopke, Philip K; Zhang, K Max

2013-01-15

A new methodology, referred to as the multi-scale structure, integrates "tailpipe-to-road" (i.e., on-road domain) and "road-to-ambient" (i.e., near-road domain) simulations to elucidate the environmental impacts of particulate emissions from traffic sources. The multi-scale structure is implemented in the CTAG model to 1) generate process-based on-road emission rates of ultrafine particles (UFPs) by explicitly simulating the effects of exhaust properties, traffic conditions, and meteorological conditions and 2) to characterize the impacts of traffic-related emissions on micro-environmental air quality near a highway intersection in Rochester, NY. The performance of CTAG, evaluated against with the field measurements, shows adequate agreement in capturing the dispersion of carbon monoxide (CO) and the number concentrations of UFPs in the near road micro-environment. As a proof-of-concept case study, we also apply CTAG to separate the relative impacts of the shutdown of a large coal-fired power plant (CFPP) and the adoption of the ultra-low-sulfur diesel (ULSD) on UFP concentrations in the intersection micro-environment. Although CTAG is still computationally expensive compared to the widely-used parameterized dispersion models, it has the potential to advance our capability to predict the impacts of UFP emissions and spatial/temporal variations of air pollutants in complex environments. Furthermore, for the on-road simulations, CTAG can serve as a process-based emission model; Combining the on-road and near-road simulations, CTAG becomes a "plume-in-grid" model for mobile emissions. The processed emission profiles can potentially improve regional air quality and climate predictions accordingly. Copyright © 2012 Elsevier B.V. All rights reserved.

Air quality impacts of distributed energy resources implemented in the northeastern United States.

PubMed

Carreras-Sospedra, Marc; Dabdub, Donald; Brouwer, Jacob; Knipping, Eladio; Kumar, Naresh; Darrow, Ken; Hampson, Anne; Hedman, Bruce

2008-07-01

Emissions from the potential installation of distributed energy resources (DER) in the place of current utility-scale power generators have been introduced into an emissions inventory of the northeastern United States. A methodology for predicting future market penetration of DER that considers economics and emission factors was used to estimate the most likely implementation of DER. The methodology results in spatially and temporally resolved emission profiles of criteria pollutants that are subsequently introduced into a detailed atmospheric chemistry and transport model of the region. The DER technology determined by the methodology includes 62% reciprocating engines, 34% gas turbines, and 4% fuel cells and other emerging technologies. The introduction of DER leads to retirement of 2625 MW of existing power plants for which emissions are removed from the inventory. The air quality model predicts maximum differences in air pollutant concentrations that are located downwind from the central power plants that were removed from the domain. Maximum decreases in hourly peak ozone concentrations due to DER use are 10 ppb and are located over the state of New Jersey. Maximum decreases in 24-hr average fine particulate matter (PM2.5) concentrations reach 3 microg/m3 and are located off the coast of New Jersey and New York. The main contribution to decreased PM2.5 is the reduction of sulfate levels due to significant reductions in direct emissions of sulfur oxides (SO(x)) from the DER compared with the central power plants removed. The scenario presented here represents an accelerated DER penetration case with aggressive emission reductions due to removal of highly emitting power plants. Such scenario provides an upper bound for air quality benefits of DER implementation scenarios.

Utilization of satellite observation of ozone and aerosols in providing initial and boundary condition for regional air quality studies

NASA Astrophysics Data System (ADS)

Pour-Biazar, Arastoo; Khan, Maudood; Wang, Lihua; Park, Yun-Hee; Newchurch, Mike; McNider, Richard T.; Liu, Xiong; Byun, Daewon W.; Cameron, Robert

2011-09-01

To demonstrate the efficacy of satellite observations in the realization of the background and transboundary transport of pollution in regional air quality modeling practices, satellite observations of ozone and aerosol optical depth were incorporated in the EPA Models-3 Community Multiscale Air Quality (CMAQ) model (http://www.cmascenter.org). Observations from Ozone Monitoring Instrument (OMI) aboard NASA's Aura satellite and AOD products from the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard Terra (EOS AM) and Aqua (EOS PM) satellites were used to specify initial and lateral boundary conditions (IC/BC) for a simulation that spanned over August 2006. The tools and techniques using the satellite data were tested in the context of current regulatory air quality modeling practices. Daily satellite observations were remapped onto the modeling domain and used as IC/BC for daily segments of a month-long simulation and the results were evaluated against surface and ozonesonde observations. Compared to the standard application of CMAQ, OMI O3 profiles significantly improved model performance in the free troposphere and MODIS aerosol products substantially improved PM2.5 predictions in the boundary layer. The utilization of satellite data for BC helped in the realization of transboundary transport of pollution and was able to explain the recirculation of pollution from Northeast Corridor to the southeastern region. Ozone in the mid- to upper-troposphere was largely dominated by transport and thus benefited most from satellite provided BC. The ozone within the boundary layer was mostly affected by fast production/loss mechanisms that are impacted by surface emissions, chemistry and removal processes and was not impacted as much. A case study for August 18-22 demonstrated that model errors in the placement of a stationary front were the main reason for errors in PM2.5 predictions as the front acted as a boundary between high and low PM2.5 concentrations.

Responses of future air quality to emission controls over North Carolina, Part I: Model evaluation for current-year simulations

NASA Astrophysics Data System (ADS)

Liu, Xiao-Huan; Zhang, Yang; Olsen, Kristen M.; Wang, Wen-Xing; Do, Bebhinn A.; Bridgers, George M.

2010-07-01

The prediction of future air quality and its responses to emission control strategies at national and state levels requires a reliable model that can replicate atmospheric observations. In this work, the Mesoscale Model (MM5) and the Community Multiscale Air Quality Modeling (CMAQ) system are applied at a 4-km horizontal grid resolution for four one-month periods, i.e., January, June, July, and August in 2002 to evaluate model performance and compare with that at 12-km. The evaluation shows skills of MM5/CMAQ that are overall consistent with current model performance. The large cold bias in temperature at 1.5 m is likely due to too cold soil initial temperatures and inappropriate snow treatments. The large overprediction in precipitation in July is due likely to too frequent afternoon convective rainfall and/or an overestimation in the rainfall intensity. The normalized mean biases and errors are -1.6% to 9.1% and 15.3-18.5% in January and -18.7% to -5.7% and 13.9-20.6% in July for max 1-h and 8-h O 3 mixing ratios, respectively, and those for 24-h average PM 2.5 concentrations are 8.3-25.9% and 27.6-38.5% in January and -57.8% to -45.4% and 46.1-59.3% in July. The large underprediction in PM 2.5 in summer is attributed mainly to overpredicted precipitation, inaccurate emissions, incomplete treatments for secondary organic aerosols, and model difficulties in resolving complex meteorology and geography. While O 3 prediction shows relatively less sensitivity to horizontal grid resolutions, PM 2.5 and its secondary components, visibility indices, and dry and wet deposition show a moderate to high sensitivity. These results have important implications for the regulatory applications of MM5/CMAQ for future air quality attainment.

Local Air Quality Conditions and Forecasts

MedlinePlus

... Monitor Location Archived Maps by Region Canada Air Quality Air Quality on Google Earth Links A-Z About AirNow AirNow International Air Quality Action Days / Alerts AirCompare Air Quality Index (AQI) ...

Modeling the Role of Alkanes, Polycyclic Aromatic Hydrocarbons, and Their Oligomers in Secondary Organic Aerosol Formation

EPA Science Inventory

A computationally efficient method to treat secondary organic aerosol (SOA) from various length and structure alkanes as well as SOA from polycyclic aromatic hydrocarbons (PAHs) is implemented in the Community Multiscale Air Quality (CMAQ) model to predict aerosol concentrations ...

Role of future scenarios in understanding deep uncertainty in long-term air quality management

EPA Science Inventory

The environment and its interactions with human systems, whether economic, social or political, are complex. Relevant drivers may disrupt system dynamics in unforeseen ways, making it difficult to predict future conditions. This kind of deep uncertainty presents a challenge to ...

A hybrid model for predicting carbon monoxide from vehicular exhausts in urban environments

NASA Astrophysics Data System (ADS)

Gokhale, Sharad; Khare, Mukesh

Several deterministic-based air quality models evaluate and predict the frequently occurring pollutant concentration well but, in general, are incapable of predicting the 'extreme' concentrations. In contrast, the statistical distribution models overcome the above limitation of the deterministic models and predict the 'extreme' concentrations. However, the environmental damages are caused by both extremes as well as by the sustained average concentration of pollutants. Hence, the model should predict not only 'extreme' ranges but also the 'middle' ranges of pollutant concentrations, i.e. the entire range. Hybrid modelling is one of the techniques that estimates/predicts the 'entire range' of the distribution of pollutant concentrations by combining the deterministic based models with suitable statistical distribution models ( Jakeman, et al., 1988). In the present paper, a hybrid model has been developed to predict the carbon monoxide (CO) concentration distributions at one of the traffic intersections, Income Tax Office (ITO), in the Delhi city, where the traffic is heterogeneous in nature and meteorology is 'tropical'. The model combines the general finite line source model (GFLSM) as its deterministic, and log logistic distribution (LLD) model, as its statistical components. The hybrid (GFLSM-LLD) model is then applied at the ITO intersection. The results show that the hybrid model predictions match with that of the observed CO concentration data within the 5-99 percentiles range. The model is further validated at different street location, i.e. Sirifort roadway. The validation results show that the model predicts CO concentrations fairly well ( d=0.91) in 10-95 percentiles range. The regulatory compliance is also developed to estimate the probability of exceedance of hourly CO concentration beyond the National Ambient Air Quality Standards (NAAQS) of India. It consists of light vehicles, heavy vehicles, three- wheelers (auto rickshaws) and two-wheelers (scooters, motorcycles, etc).

Impact of Ozone Radiative Feedbacks on Global Weather Forecasting

NASA Astrophysics Data System (ADS)

Ivanova, I.; de Grandpré, J.; Rochon, Y. J.; Sitwell, M.

2017-12-01

A coupled Chemical Data Assimilation system for ozone is being developed at Environment and Climate Change Canada (ECCC) with the goals to improve the forecasting of UV index and the forecasting of air quality with the Global Environmental Multi-scale (GEM) Model for Air quality and Chemistry (MACH). Furthermore, this system provides an opportunity to evaluate the benefit of ozone assimilation for improving weather forecasting with the ECCC Global Deterministic Prediction System (GDPS) for Numerical Weather Prediction (NWP). The present UV index forecasting system uses a statistical approach for evaluating the impact of ozone in clear-sky and cloudy conditions, and the use of real-time ozone analysis and ozone forecasts is highly desirable. Improving air quality forecasting with GEM-MACH further necessitates the development of integrated dynamical-chemical assimilation system. Upon its completion, real-time ozone analysis and ozone forecasts will also be available for piloting the regional air quality system, and for the computation of ozone heating rates, in replacement of the monthly mean ozone distribution currently used in the GDPS. Experiments with ozone radiative feedbacks were run with the GDPS at 25km resolution and 84 levels with a lid at 0.1 hPa and were initialized with ozone analysis that has assimilated total ozone column from OMI, OMPS, and GOME satellite instruments. The results show that the use of prognostic ozone for the computation of the heating/cooling rates has a significant impact on the temperature distribution throughout the stratosphere and upper troposphere regions. The impact of ozone assimilation is especially significant in the tropopause region, where ozone heating in the infrared wavelengths is important and ozone lifetime is relatively long. The implementation of the ozone radiative feedback in the GDPS requires addressing various issues related to model biases (temperature and humidity) and biases in equilibrium state (ozone mixing ratio, air temperature and overhead column ozone) used for the calculation of the linearized photochemical production and loss of ozone. Furthermore the radiative budget in the tropopause region is strongly affected by water vapor cooling, which impact requires further evaluation for the use in chemically coupled operational NWP systems.

DEA-I: A Globally Configurable Open Source Software Package in Support of Air Quality Forecasts

NASA Astrophysics Data System (ADS)

Davies, J.; Strabala, K.; Pierce, R.; Huang, H.; Schiffer, E.

2012-12-01

During September 2003, a team of NASA, NOAA, and EPA researchers demonstrated a prototype for using Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol optical depth retrievals in daily air quality forecasts; this became known as IDEA (Infusing satellite Data into Environmental Applications). IDEA was part of the NASA Applied Sciences Program strategy to demonstrate practical uses of NASA-sponsored observations from space and predictions. Following its successful demonstration an export version of IDEA, known as IDEA International (IDEA-I), has now been released. IDEA-I supports the Global Earth Observation Systems of Systems (GEOSS) Group on Earth Observations (GEO) Health Societal Benefit Area (SBA) and is being developed within the framework of the GEO Earth Observations in Decision Support Call for Proposals. The vehicle for IDEA-I release is the International MODIS and AIRS (Atmospheric Infrared Sounder) Processing Package (IMAPP), developed at the Space Science and Engineering Center, University of Wisconsin-Madison (SSEC/UW-Madison). IMAPP is a NASA-funded and freely-distributed software package which allows any ground station capable of receiving direct broadcast from Terra or Aqua to produce calibrated and geolocated radiances, and a suite of environmental products, of which the IDEA-I 48-hour forward trajectory prediction of high aerosol events is now a part. IDEA-I provides a tool for linking ground-based and satellite capabilities to support international air quality forecasting activities and is to be demonstrated internationally through user training and impact evaluation via a series of IMAPP workshops. This presentation describes the IMAPP implementation of IDEA-I in terms of its simple installation and configuration, and through examples of its operation in several regions known for periodic high aerosol events.; Screen capture of the University of Wisconsin implementation of the real-time direct broadcast IDEA-I Air Quality monitoring website. This example uses Terra MODIS Aerosol Optical Depth retrievals to identify regions of high aerosol concentrations. A trajectory model is then run that provide a forecast of the horizontal and vertical movement of the aerosols over the next 48 hours.

Parametric Analysis of Surveillance Quality and Level and Quality of Intent Information and Their Impact on Conflict Detection Performance

NASA Technical Reports Server (NTRS)

Guerreiro, Nelson M.; Butler, Ricky W.; Hagen, George E.; Maddalon, Jeffrey M.; Lewis, Timothy A.

2016-01-01

A loss-of-separation (LOS) is said to occur when two aircraft are spatially too close to one another. A LOS is the fundamental unsafe event to be avoided in air traffic management and conflict detection (CD) is the function that attempts to predict these LOS events. In general, the effectiveness of conflict detection relates to the overall safety and performance of an air traffic management concept. An abstract, parametric analysis was conducted to investigate the impact of surveillance quality, level of intent information, and quality of intent information on conflict detection performance. The data collected in this analysis can be used to estimate the conflict detection performance under alternative future scenarios or alternative allocations of the conflict detection function, based on the quality of the surveillance and intent information under those conditions.Alternatively, this data could also be used to estimate the surveillance and intent information quality required to achieve some desired CD performance as part of the design of a new separation assurance system.

Autoregressive spatially varying coefficients model for predicting daily PM2.5 using VIIRS satellite AOT

NASA Astrophysics Data System (ADS)

Schliep, E. M.; Gelfand, A. E.; Holland, D. M.

2015-12-01

There is considerable demand for accurate air quality information in human health analyses. The sparsity of ground monitoring stations across the United States motivates the need for advanced statistical models to predict air quality metrics, such as PM2.5, at unobserved sites. Remote sensing technologies have the potential to expand our knowledge of PM2.5 spatial patterns beyond what we can predict from current PM2.5 monitoring networks. Data from satellites have an additional advantage in not requiring extensive emission inventories necessary for most atmospheric models that have been used in earlier data fusion models for air pollution. Statistical models combining monitoring station data with satellite-obtained aerosol optical thickness (AOT), also referred to as aerosol optical depth (AOD), have been proposed in the literature with varying levels of success in predicting PM2.5. The benefit of using AOT is that satellites provide complete gridded spatial coverage. However, the challenges involved with using it in fusion models are (1) the correlation between the two data sources varies both in time and in space, (2) the data sources are temporally and spatially misaligned, and (3) there is extensive missingness in the monitoring data and also in the satellite data due to cloud cover. We propose a hierarchical autoregressive spatially varying coefficients model to jointly model the two data sources, which addresses the foregoing challenges. Additionally, we offer formal model comparison for competing models in terms of model fit and out of sample prediction of PM2.5. The models are applied to daily observations of PM2.5 and AOT in the summer months of 2013 across the conterminous United States. Most notably, during this time period, we find small in-sample improvement incorporating AOT into our autoregressive model but little out-of-sample predictive improvement.

Application of XGBoost algorithm in hourly PM2.5 concentration prediction

NASA Astrophysics Data System (ADS)

Pan, Bingyue

2018-02-01

In view of prediction techniques of hourly PM2.5 concentration in China, this paper applied the XGBoost(Extreme Gradient Boosting) algorithm to predict hourly PM2.5 concentration. The monitoring data of air quality in Tianjin city was analyzed by using XGBoost algorithm. The prediction performance of the XGBoost method is evaluated by comparing observed and predicted PM2.5 concentration using three measures of forecast accuracy. The XGBoost method is also compared with the random forest algorithm, multiple linear regression, decision tree regression and support vector machines for regression models using computational results. The results demonstrate that the XGBoost algorithm outperforms other data mining methods.

Particle loading rates for HVAC filters, heat exchangers, and ducts.

PubMed

Waring, M S; Siegel, J A

2008-06-01

The rate at which airborne particulate matter deposits onto heating, ventilation, and air-conditioning (HVAC) components is important from both indoor air quality (IAQ) and energy perspectives. This modeling study predicts size-resolved particle mass loading rates for residential and commercial filters, heat exchangers (i.e. coils), and supply and return ducts. A parametric analysis evaluated the impact of different outdoor particle distributions, indoor emission sources, HVAC airflows, filtration efficiencies, coils, and duct system complexities. The median predicted residential and commercial loading rates were 2.97 and 130 g/m(2) month for the filter loading rates, 0.756 and 4.35 g/m(2) month for the coil loading rates, 0.0051 and 1.00 g/month for the supply duct loading rates, and 0.262 g/month for the commercial return duct loading rates. Loading rates are more dependent on outdoor particle distributions, indoor sources, HVAC operation strategy, and filtration than other considered parameters. The results presented herein, once validated, can be used to estimate filter changing and coil cleaning schedules, energy implications of filter and coil loading, and IAQ impacts associated with deposited particles. The results in this paper suggest important factors that lead to particle deposition on HVAC components in residential and commercial buildings. This knowledge informs the development and comparison of control strategies to limit particle deposition. The predicted mass loading rates allow for the assessment of pressure drop and indoor air quality consequences that result from particle mass loading onto HVAC system components.

ManUniCast: A Community Weather and Air-Quality Forecasting Teaching Portal

NASA Astrophysics Data System (ADS)

Schultz, David M.; Anderson, Stuart; Fairman, Jonathan G.; Lowe, Douglas; McFiggans, Gordon; Lee, Elsa; Seo-Zindy, Ryo

2014-05-01

Manunicast was borne out of the needs of our teaching program: students were entering a world where environmental prediction via numerical model was an essential skill, but were not exposed to the production or output of such models. Our site is an educational testbed to explain to students and the public how weather, air-quality, and air-chemistry forecasts are made using real-time predictions as examples. As far as we know, this site provides the first freely available real-time predictions for the UK. We perform two simulations a day over three domains using the most popular, freely available, community atmospheric mesoscale and chemistry models WRF-ARW and WRF-Chem: 1. a WRF-ARW domain over the North Atlantic and western Europe (20-km horizontal grid spacing) 2. a WRF-ARW domain over the UK and Ireland (4-km grid spacing, nested within the 20-km domain) 3. a WRF-Chem domain over the UK and Ireland (12-km grid spacing) Called ManUniCast (Manchester University Forecast), we offer a suite of products from horizontal maps, time series at stations (meteograms), skew-T-logp charts, and cross sections to help students better visualize the weather and the relationships between the various fields more effectively, specifically through the ability to overlay and fade between different plotted products. This presentation discusses how we funded and built ManUniCast, the struggles we faced, and its use in our classes.

Ensemble classification for identifying neighbourhood sources of fugitive dust and associations with observed PM10

NASA Astrophysics Data System (ADS)

Khuluse-Makhanya, Sibusisiwe; Stein, Alfred; Breytenbach, André; Gxumisa, Athi; Dudeni-Tlhone, Nontembeko; Debba, Pravesh

2017-10-01

In urban areas the deterioration of air quality as a result of fugitive dust receives less attention than the more prominent traffic and industrial emissions. We assessed whether fugitive dust emission sources in the neighbourhood of an air quality monitor are predictors of ambient PM10 concentrations on days characterized by strong local winds. An ensemble maximum likelihood method is developed for land cover mapping in the vicinity of an air quality station using SPOT 6 multi-spectral images. The ensemble maximum likelihood classifier is developed through multiple training iterations for improved accuracy of the bare soil class. Five primary land cover classes are considered, namely built-up areas, vegetation, bare soil, water and 'mixed bare soil' which denotes areas where soil is mixed with either vegetation or synthetic materials. Preliminary validation of the ensemble classifier for the bare soil class results in an accuracy range of 65-98%. Final validation of all classes results in an overall accuracy of 78%. Next, cluster analysis and a varying intercepts regression model are used to assess the statistical association between land cover, a fugitive dust emissions proxy and observed PM10. We found that land cover patterns in the neighbourhood of an air quality station are significant predictors of observed average PM10 concentrations on days when wind speeds are conducive for dust emissions. This study concludes that in the absence of an emissions inventory for ambient particulate matter, PM10 emitted from dust reservoirs can be statistically accounted for by land cover characteristics. This supports the use of land cover data for improved prediction of PM10 at locations without air quality monitoring stations.

Higher Surface Ozone Concentrations Over the Chesapeake Bay than Over the Adjacent Land: Observations and Models from the DISCOVER-AQ and CBODAQ Campaigns

NASA Technical Reports Server (NTRS)

Goldberg, Daniel L.; Loughner, Christopher P.; Tzortziou, Maria; Stehr, Jeffrey W.; Pickering, Kenneth E.; Marufu, Lackson T.; Dickerson, Russell R.

2013-01-01

Air quality models, such as the Community Multiscale Air Quality (CMAQ) model, indicate decidedly higher ozone near the surface of large interior water bodies, such as the Great Lakes and Chesapeake Bay. In order to test the validity of the model output, we performed surface measurements of ozone (O3) and total reactive nitrogen (NOy) on the 26-m Delaware II NOAA Small Research Vessel experimental (SRVx), deployed in the Chesapeake Bay for 10 daytime cruises in July 2011 as part of NASA's GEO-CAPE CBODAQ oceanographic field campaign in conjunction with NASA's DISCOVER-AQ air quality field campaign. During this 10-day period, the EPA O3 regulatory standard of 75 ppbv averaged over an 8-h period was exceeded four times over water while ground stations in the area only exceeded the standard at most twice. This suggests that on days when the Baltimore/Washington region is in compliance with the EPA standard, air quality over the Chesapeake Bay might exceed the EPA standard. Ozone observations over the bay during the afternoon were consistently 10-20% higher than the closest upwind ground sites during the 10-day campaign; this pattern persisted during good and poor air quality days. A lower boundary layer, reduced cloud cover, slower dry deposition rates, and other lesser mechanisms, contribute to the local maximum of ozone over the Chesapeake Bay. Observations from this campaign were compared to a CMAQ simulation at 1.33 km resolution. The model is able to predict the regional maximum of ozone over the Chesapeake Bay accurately, but NOy concentrations are significantly overestimated. Explanations for the overestimation of NOy in the model simulations are also explored

Effects of Large-Scale Solar Installations on Dust Mobilization and Air Quality

NASA Astrophysics Data System (ADS)

Pratt, J. T.; Singh, D.; Diffenbaugh, N. S.

2012-12-01

Large-scale solar projects are increasingly being developed worldwide and many of these installations are located in arid, desert regions. To examine the effects of these projects on regional dust mobilization and air quality, we analyze aerosol product data from NASA's Multi-angle Imaging Spectroradiometer (MISR) at annual and seasonal time intervals near fifteen photovoltaic and solar thermal stations ranging from 5-200 MW (12-4,942 acres) in size. The stations are distributed over eight different countries and were chosen based on size, location and installation date; most of the installations are large-scale, took place in desert climates and were installed between 2006 and 2010. We also consider air quality measurements of particulate matter between 2.5 and 10 micrometers (PM10) from the Environmental Protection Agency (EPA) monitoring sites near and downwind from the project installations in the U.S. We use monthly wind data from the NOAA's National Center for Atmospheric Prediction (NCEP) Global Reanalysis to select the stations downwind from the installations, and then perform statistical analysis on the data to identify any significant changes in these quantities. We find that fourteen of the fifteen regions have lower aerosol product after the start of the installations as well as all six PM10 monitoring stations showing lower particulate matter measurements after construction commenced. Results fail to show any statistically significant differences in aerosol optical index or PM10 measurements before and after the large-scale solar installations. However, many of the large installations are very recent, and there is insufficient data to fully understand the long-term effects on air quality. More data and higher resolution analysis is necessary to better understand the relationship between large-scale solar, dust and air quality.

Higher surface ozone concentrations over the Chesapeake Bay than over the adjacent land: Observations and models from the DISCOVER-AQ and CBODAQ campaigns

NASA Astrophysics Data System (ADS)

Goldberg, Daniel L.; Loughner, Christopher P.; Tzortziou, Maria; Stehr, Jeffrey W.; Pickering, Kenneth E.; Marufu, Lackson T.; Dickerson, Russell R.

2014-02-01

Air quality models, such as the Community Multiscale Air Quality (CMAQ) model, indicate decidedly higher ozone near the surface of large interior water bodies, such as the Great Lakes and Chesapeake Bay. In order to test the validity of the model output, we performed surface measurements of ozone (O3) and total reactive nitrogen (NOy) on the 26-m Delaware II NOAA Small Research Vessel experimental (SRVx), deployed in the Chesapeake Bay for 10 daytime cruises in July 2011 as part of NASA's GEO-CAPE CBODAQ oceanographic field campaign in conjunction with NASA's DISCOVER-AQ air quality field campaign. During this 10-day period, the EPA O3 regulatory standard of 75 ppbv averaged over an 8-h period was exceeded four times over water while ground stations in the area only exceeded the standard at most twice. This suggests that on days when the Baltimore/Washington region is in compliance with the EPA standard, air quality over the Chesapeake Bay might exceed the EPA standard. Ozone observations over the bay during the afternoon were consistently 10-20% higher than the closest upwind ground sites during the 10-day campaign; this pattern persisted during good and poor air quality days. A lower boundary layer, reduced cloud cover, slower dry deposition rates, and other lesser mechanisms, contribute to the local maximum of ozone over the Chesapeake Bay. Observations from this campaign were compared to a CMAQ simulation at 1.33 km resolution. The model is able to predict the regional maximum of ozone over the Chesapeake Bay accurately, but NOy concentrations are significantly overestimated. Explanations for the overestimation of NOy in the model simulations are also explored.

Prediction of CO concentrations based on a hybrid Partial Least Square and Support Vector Machine model

NASA Astrophysics Data System (ADS)

Yeganeh, B.; Motlagh, M. Shafie Pour; Rashidi, Y.; Kamalan, H.

2012-08-01

Due to the health impacts caused by exposures to air pollutants in urban areas, monitoring and forecasting of air quality parameters have become popular as an important topic in atmospheric and environmental research today. The knowledge on the dynamics and complexity of air pollutants behavior has made artificial intelligence models as a useful tool for a more accurate pollutant concentration prediction. This paper focuses on an innovative method of daily air pollution prediction using combination of Support Vector Machine (SVM) as predictor and Partial Least Square (PLS) as a data selection tool based on the measured values of CO concentrations. The CO concentrations of Rey monitoring station in the south of Tehran, from Jan. 2007 to Feb. 2011, have been used to test the effectiveness of this method. The hourly CO concentrations have been predicted using the SVM and the hybrid PLS-SVM models. Similarly, daily CO concentrations have been predicted based on the aforementioned four years measured data. Results demonstrated that both models have good prediction ability; however the hybrid PLS-SVM has better accuracy. In the analysis presented in this paper, statistic estimators including relative mean errors, root mean squared errors and the mean absolute relative error have been employed to compare performances of the models. It has been concluded that the errors decrease after size reduction and coefficients of determination increase from 56 to 81% for SVM model to 65-85% for hybrid PLS-SVM model respectively. Also it was found that the hybrid PLS-SVM model required lower computational time than SVM model as expected, hence supporting the more accurate and faster prediction ability of hybrid PLS-SVM model.

Characterizing the impact of projected changes in climate and air quality on human exposures to ozone.

PubMed

Dionisio, Kathie L; Nolte, Christopher G; Spero, Tanya L; Graham, Stephen; Caraway, Nina; Foley, Kristen M; Isaacs, Kristin K

2017-05-01

The impact of climate change on human and environmental health is of critical concern. Population exposures to air pollutants both indoors and outdoors are influenced by a wide range of air quality, meteorological, behavioral, and housing-related factors, many of which are also impacted by climate change. An integrated methodology for modeling changes in human exposures to tropospheric ozone (O 3 ) owing to potential future changes in climate and demographics was implemented by linking existing modeling tools for climate, weather, air quality, population distribution, and human exposure. Human exposure results from the Air Pollutants Exposure Model (APEX) for 12 US cities show differences in daily maximum 8-h (DM8H) exposure patterns and levels by sex, age, and city for all scenarios. When climate is held constant and population demographics are varied, minimal difference in O 3 exposures is predicted even with the most extreme demographic change scenario. In contrast, when population is held constant, we see evidence of substantial changes in O 3 exposure for the most extreme change in climate. Similarly, we see increases in the percentage of the population in each city with at least one O 3 exposure exceedance above 60 p.p.b and 70 p.p.b thresholds for future changes in climate. For these climate and population scenarios, the impact of projected changes in climate and air quality on human exposure to O 3 are much larger than the impacts of changing demographics. These results indicate the potential for future changes in O 3 exposure as a result of changes in climate that could impact human health.

Environmental impacts and sustainability of egg production systems.

PubMed

Xin, H; Gates, R S; Green, A R; Mitloehner, F M; Moore, P A; Wathes, C M

2011-01-01

As part of a systemic assessment toward social sustainability of egg production, we have reviewed current knowledge about the environmental impacts of egg production systems and identified topics requiring further research. Currently, we know that 1) high-rise cage houses generally have poorer air quality and emit more ammonia than manure belt (MB) cage houses; 2) manure removal frequency in MB houses greatly affects ammonia emissions; 3) emissions from manure storage are largely affected by storage conditions, including ventilation rate, manure moisture content, air temperature, and stacking profile; 4) more baseline data on air emissions from high-rise and MB houses are being collected in the United States to complement earlier measurements; 5) noncage houses generally have poorer air quality (ammonia and dust levels) than cage houses; 6) noncage houses tend to be colder during cold weather due to a lower stocking density than caged houses, leading to greater feed and fuel energy use; 7) hens in noncage houses are less efficient in resource (feed, energy, and land) utilization, leading to a greater carbon footprint; 8) excessive application of hen manure to cropland can lead to nutrient runoff to water bodies; 9) hen manure on open (free) range may be subject to runoff during rainfall, although quantitative data are lacking; 10) mitigation technologies exist to reduce generation and emission of noxious gases and dust; however, work is needed to evaluate their economic feasibility and optimize design; and 11) dietary modification shows promise for mitigating emissions. Further research is needed on 1) indoor air quality, barn emissions, thermal conditions, and energy use in alternative hen housing systems (1-story floor, aviary, and enriched cage systems), along with conventional housing systems under different production conditions; 2) environmental footprint for different US egg production systems through life cycle assessment; 3) practical means to mitigate air emissions from different production systems; 4) process-based models for predicting air emissions and their fate; and 5) the interactions between air quality, housing system, worker health, and animal health and welfare.

QUANTIFYING THE CLIMATE, AIR QUALITY AND HEALTH BENEFITS OF IMPROVED COOKSTOVES: AN INTEGRATED LABORATORY, FIELD AND MODELING STUDY

EPA Science Inventory

Expected results and outputs include: extensive dataset of in-field and laboratory emissions data for traditional and improved cookstoves; parameterization to predict cookstove emissions from drive cycle data; indoor and personal exposure data for traditional and improved cook...

IMPACT OF AN UPDATED CARBON BOND MECHANISM ON PREDICTIONS FROM THE CMAQ MODELING SYSTEM: PRELIMINARY ASSESSMENT

EPA Science Inventory

An updated and expanded Carbon Bond mechanism (CB05) has been incorporated into the Community Multiscale Air Quality modeling system to more accurately simulate wintertime, pristine, and high altitude situations. The CB05 mechanism has nearly twice the number of reactions compare...

Metadata Creation Tool Content Template For Data Stewards

EPA Science Inventory

A space-time Bayesian fusion model (McMillan, Holland, Morara, and Feng, 2009) is used to provide daily, gridded predictive PM_2.5 (daily average) and O₃ (daily 8-hr maximum) surfaces for 2001-2005. The fusion model uses both air quality monitoring data from ...

MULTI-SCALE GRID DEFINITION IMPACTS ON REGIONAL, THREE-DIMENSIONAL AIR QUALITY MODEL PREDICTIONS AND PERFORMANCE. (R825821)

EPA Science Inventory

The perspectives, information and conclusions conveyed in research project abstracts, progress reports, final reports, journal abstracts and journal publications convey the viewpoints of the principal investigator and may not represent the views and policies of ORD and EPA. Concl...

A TEST OF THERMODYNAMIC EQUILIBRIUM MODELS AND 3-D AIR QUALITY MODELS FOR PREDICTIONS OF AEROSOL NO3-

EPA Science Inventory

The inorganic species of sulfate, nitrate and ammonium constitute a major fraction of atmospheric aerosols. The behavior of nitrate is one of the most intriguing aspects of inorganic atmospheric aerosols because particulate nitrate concentrations depend not only on the amount of ...

THE IMPACT OF THE CONGESTION CHARGING SCHEME ON AIR QUALITY IN LONDON

EPA Science Inventory

The modeling studies predicted small changes in both emissions and ambient concentrations of NOx, NO2, and PM10 across London that could be related to the implementation of the CCS, although the effects within the CCZ were projected to be more pronounced than elsewhere. The...

National Centers for Environmental Prediction

Science.gov Websites

Reference List Table of Contents NCEP OPERATIONAL MODEL FORECAST GRAPHICS PARALLEL/EXPERIMENTAL MODEL Developmental Air Quality Forecasts and Verification Back to Table of Contents 2. PARALLEL/EXPERIMENTAL GRAPHICS VERIFICATION (GRID VS.OBS) WEB PAGE (NCEP EXPERIMENTAL PAGE, INTERNAL USE ONLY) Interactive web page tool for

DEVELOPMENT OF A MICROSCALE EMISSION FACTOR MODEL FOR PARTICULATE MATTER (MICROFACPM) FOR PREDICTING REAL TIME MOTOR VEHICLE EMISSIONS

EPA Science Inventory

Health risk evaluation needs precise measurement and modeling of human exposures in microenvironments to support review of current air quality standards. The particulate matter emissions from motor vehicles are a major component of human exposures in urban microenvironments. Cu...

Predicting air quality near roadways through the application of a Gaussian Puff Model to moving sources

DOT National Transportation Integrated Search

2000-06-19

The Environmental Protection Agency (EPA) currently recommends the use of CALINE3 or CAL3QHC for modeling the dispersion of carbon monoxide (CO) near roadways. These models treat vehicles as part of a line source such that the emissions are homogeneo...

Influence of the choice of gas-phase mechanism on predictions of key gaseous pollutants during the AQMEII phase-2 intercomparison

EPA Science Inventory

The formulations of tropospheric gas-phase chemistry (“mechanisms”)used in the regional-scale chemistry-transport models participating in theAir Quality Modelling Evaluation International Initiative (AQMEII) Phase2 are intercompared by the means of box model studies. Simulations ...

Associations between summertime ambient pollutants and respiratory morbidity in New York City: Comparison of results using ambient concentrations versus predicted exposures

EPA Science Inventory

Epidemiological analyses of air quality often estimate human exposure from ambient monitoring data, potentially leading to exposure misclassification and subsequent bias in estimated health risks. To investigate this, we conducted a case-crossover study of summertime ambient ozon...

Predicting Nitrogen in Streams : A Comparison of Two Estimates of Fertilizer Application

EPA Science Inventory

Decision makers frequently rely on water and air quality models to develop nutrient management strategies. Obviously, the results of these models (e.g., SWAT, SPARROW, CMAQ) are only as good as the nutrient source input data and recently the Nutrient Innovations Task Group has ca...

The Cornucopian Fallacies: The Myth of Perpetual Growth.

ERIC Educational Resources Information Center

Grant, Lindsey

1983-01-01

Julian Simon and Herman Kahn argue that population growth is good, not bad; they ignore or dismiss critical environmental issues. Fallacies in their arguments about predicting the future from the past, climate change, population growth, air quality, resource expansion, the finite earth, and technological growth are examined. (SR)

EMISSIONS INVENTORY OF PM 2.5 TRACE ELEMENTS ACROSS THE U.S.

EPA Science Inventory

This abstract describes work done to speciate PM_2.5 emissions into emissions of trace metals to enable concentrations of metal species to be predicted by air quality models. Methods are described and initial results are presented. A technique for validating the resul...

Interpreting Predictions from the SAPRC07 Mechanism Based on Regional and Continental Simulations

EPA Science Inventory

The SAPRC07T mechanism is implemented and evaluated in the CMAQ air quality model. The implementation is described and tested with simulations over the United States for two periods. The evaluation compares results against observations for ozone and particulate matter as well as ...

“Impact of RACM2, halogen chemistry, and updated ozonedeposition velocity on hemispheric ozone predictions”

EPA Science Inventory

We incorporate the Regional Atmospheric Chemistry Mechanism (RACM2) into the Community Multiscale Air Quality (CMAQ) hemispheric model and compare model predictions to those obtained using the existing Carbon Bond chemical mechanism with updated toluene chemistry (CB05TU). The RA...

Developing a Method for Resolving NOx Emission Inventory Biases Using Discrete Kalman Filter Inversion, Direct Sensitivities, and Satellite-Based Columns

EPA Science Inventory

An inverse method was developed to integrate satellite observations of atmospheric pollutant column concentrations and direct sensitivities predicted by a regional air quality model in order to discern biases in the emissions of the pollutant precursors.

Application of the WEPS and SWEEP models to non-agricultural disturbed lands

USDA-ARS?s Scientific Manuscript database

Wind erosion not only affects agricultural productivity but also soil, air, and water quality. Dust and specifically particulate matter = 10 µm (PM-10) has adverse effects on respiratory health and also reduces visibility along roadways, resulting in auto accidents. The Wind Erosion Prediction Syste...

Performance and evaluation of a coupled prognostic model TAPM over a mountainous complex terrain industrial area

NASA Astrophysics Data System (ADS)

Matthaios, Vasileios N.; Triantafyllou, Athanasios G.; Albanis, Triantafyllos A.; Sakkas, Vasileios; Garas, Stelios

2018-05-01

Atmospheric modeling is considered an important tool with several applications such as prediction of air pollution levels, air quality management, and environmental impact assessment studies. Therefore, evaluation studies must be continuously made, in order to improve the accuracy and the approaches of the air quality models. In the present work, an attempt is made to examine the air pollution model (TAPM) efficiency in simulating the surface meteorology, as well as the SO2 concentrations in a mountainous complex terrain industrial area. Three configurations under different circumstances, firstly with default datasets, secondly with data assimilation, and thirdly with updated land use, ran in order to investigate the surface meteorology for a 3-year period (2009-2011) and one configuration applied to predict SO2 concentration levels for the year of 2011.The modeled hourly averaged meteorological and SO2 concentration values were statistically compared with those from five monitoring stations across the domain to evaluate the model's performance. Statistical measures showed that the surface temperature and relative humidity are predicted well in all three simulations, with index of agreement (IOA) higher than 0.94 and 0.70 correspondingly, in all monitoring sites, while an overprediction of extreme low temperature values is noted, with mountain altitudes to have an important role. However, the results also showed that the model's performance is related to the configuration regarding the wind. TAPM default dataset predicted better the wind variables in the center of the simulation than in the boundaries, while improvement in the boundary horizontal winds implied the performance of TAPM with updated land use. TAPM assimilation predicted the wind variables fairly good in the whole domain with IOA higher than 0.83 for the wind speed and higher than 0.85 for the horizontal wind components. Finally, the SO2 concentrations were assessed by the model with IOA varied from 0.37 to 0.57, mostly dependent on the grid/monitoring station of the simulated domain. The present study can be used, with relevant adaptations, as a user guideline for future conducting simulations in mountainous complex terrain.

An indoor air quality-pharmacokinetic simulation of passive inhalation of marijuana smoke and the resultant buildup of 11-nor-delta-9-tetrahydrocannabinol-9-carboxylic acid in urine.

PubMed

Giardino, N J

1997-03-01

In military courts of law, the good soldier defense is often used by the defendant to explain the presence of 11-nor-delta-9-tetrahydrocannabinol-9-carboxylic acid in urine (hereafter referred to as THCA) above the Department of Defense (DOD) established limit of 15 ng/mL. The defense will contend the defendant unwittingly breathed side-stream marijuana smoke, thus resulting in the presence of THCA in the defendant's urine. The purpose of this work was to link an indoor air quality model (IAQ) with a pharmacokinetic (PK) model to predict a passive marijuana smoker's resultant concentration of the major urinary metabolite THCA.

Field Studies Delve Into the Intricacies of Mountain Weather

NASA Astrophysics Data System (ADS)

Fernando, Harindra J. S.; Pardyjak, Eric R.

2013-09-01

Mountain meteorology, in particular weather prediction in complex (rugged) terrain, is emerging as an important topic for science and society. Large urban settlements such as Los Angeles, Hong Kong, and Rio de Janeiro have grown within or in the shadow of complex terrain, and managing the air quality of such cities requires a good understanding of the air flow patterns that spill off of mountains. On a daily time scale, the interconnected engineered and natural systems that sustain urban metabolism and quality of life are affected by weather [Fernando, 2010]. Further, recent military engagements in remote mountainous areas have heightened the need for better weather predictions—alpine warfare is considered to be one of the most dangerous types of combat.

GNAQPMS v1.1: accelerating the Global Nested Air Quality Prediction Modeling System (GNAQPMS) on Intel Xeon Phi processors

NASA Astrophysics Data System (ADS)

Wang, Hui; Chen, Huansheng; Wu, Qizhong; Lin, Junmin; Chen, Xueshun; Xie, Xinwei; Wang, Rongrong; Tang, Xiao; Wang, Zifa

2017-08-01

The Global Nested Air Quality Prediction Modeling System (GNAQPMS) is the global version of the Nested Air Quality Prediction Modeling System (NAQPMS), which is a multi-scale chemical transport model used for air quality forecast and atmospheric environmental research. In this study, we present the porting and optimisation of GNAQPMS on a second-generation Intel Xeon Phi processor, codenamed Knights Landing (KNL). Compared with the first-generation Xeon Phi coprocessor (codenamed Knights Corner, KNC), KNL has many new hardware features such as a bootable processor, high-performance in-package memory and ISA compatibility with Intel Xeon processors. In particular, we describe the five optimisations we applied to the key modules of GNAQPMS, including the CBM-Z gas-phase chemistry, advection, convection and wet deposition modules. These optimisations work well on both the KNL 7250 processor and the Intel Xeon E5-2697 V4 processor. They include (1) updating the pure Message Passing Interface (MPI) parallel mode to the hybrid parallel mode with MPI and OpenMP in the emission, advection, convection and gas-phase chemistry modules; (2) fully employing the 512 bit wide vector processing units (VPUs) on the KNL platform; (3) reducing unnecessary memory access to improve cache efficiency; (4) reducing the thread local storage (TLS) in the CBM-Z gas-phase chemistry module to improve its OpenMP performance; and (5) changing the global communication from writing/reading interface files to MPI functions to improve the performance and the parallel scalability. These optimisations greatly improved the GNAQPMS performance. The same optimisations also work well for the Intel Xeon Broadwell processor, specifically E5-2697 v4. Compared with the baseline version of GNAQPMS, the optimised version was 3.51 × faster on KNL and 2.77 × faster on the CPU. Moreover, the optimised version ran at 26 % lower average power on KNL than on the CPU. With the combined performance and energy improvement, the KNL platform was 37.5 % more efficient on power consumption compared with the CPU platform. The optimisations also enabled much further parallel scalability on both the CPU cluster and the KNL cluster scaled to 40 CPU nodes and 30 KNL nodes, with a parallel efficiency of 70.4 and 42.2 %, respectively.

Benzene patterns in different urban environments and a prediction model for benzene rates based on NOx values

NASA Astrophysics Data System (ADS)

Paz, Shlomit; Goldstein, Pavel; Kordova-Biezuner, Levana; Adler, Lea

2017-04-01

Exposure to benzene has been associated with multiple severe impacts on health. This notwithstanding, at most monitoring stations, benzene is not monitored on a regular basis. The aims of the study were to compare benzene rates in different urban environments (region with heavy traffic and industrial region), to analyse the relationship between benzene and meteorological parameters in a Mediterranean climate type, to estimate the linkages between benzene and NOx and to suggest a prediction model for benzene rates based on NOx levels in order contribute to a better estimation of benzene. Data were used from two different monitoring stations, located on the eastern Mediterranean coast: 1) a traffic monitoring station in Tel Aviv, Israel (TLV) located in an urban region with heavy traffic; 2) a general air quality monitoring station in Haifa Bay (HIB), located in Israel's main industrial region. At each station, hourly, daily, monthly, seasonal, and annual data of benzene, NOx, mean temperature, relative humidity, inversion level, and temperature gradient were analysed over three years: 2008, 2009, and 2010. A prediction model for benzene rates based on NOx levels (which are monitored regularly) was developed to contribute to a better estimation of benzene. The severity of benzene pollution was found to be considerably higher at the traffic monitoring station (TLV) than at the general air quality station (HIB), despite the location of the latter in an industrial area. Hourly, daily, monthly, seasonal, and annual patterns have been shown to coincide with anthropogenic activities (traffic), the day of the week, and atmospheric conditions. A strong correlation between NOx and benzene allowed the development of a prediction model for benzene rates, based on NOx, the day of the week, and the month. The model succeeded in predicting the benzene values throughout the year (except for September). The severity of benzene pollution was found to be considerably higher at the traffic station (TLV) than at the general air quality station (HIB), despite being located in an industrial area. Hourly, daily, seasonal, and annual patterns of benzene rates have been shown to coincide with anthropogenic activities (traffic), day of the week, and atmospheric conditions. A prediction model for benzene rates was developed, based on NOx, the day of the week, and the month. The model suggested in this study might be useful for identifying potential risk of benzene in other urban environments.

InMAP: A model for air pollution interventions

DOE PAGES

Tessum, Christopher W.; Hill, Jason D.; Marshall, Julian D.; ...

2017-04-19

Mechanistic air pollution modeling is essential in air quality management, yet the extensive expertise and computational resources required to run most models prevent their use in many situations where their results would be useful. We present InMAP (Intervention Model for Air Pollution), which offers an alternative to comprehensive air quality models for estimating the air pollution health impacts of emission reductions and other potential interventions. InMAP estimates annual-average changes in primary and secondary fine particle (PM2.5) concentrations—the air pollution outcome generally causing the largest monetized health damages–attributable to annual changes in precursor emissions. InMAP leverages pre-processed physical and chemical informationmore » from the output of a state-of-the-science chemical transport model and a variable spatial resolution computational grid to perform simulations that are several orders of magnitude less computationally intensive than comprehensive model simulations. In comparisons we run, InMAP recreates comprehensive model predictions of changes in total PM2.5 concentrations with population-weighted mean fractional bias (MFB) of -17% and population-weighted R2 = 0.90. Although InMAP is not specifically designed to reproduce total observed concentrations, it is able to do so within published air quality model performance criteria for total PM2.5. Potential uses of InMAP include studying exposure, health, and environmental justice impacts of potential shifts in emissions for annual-average PM2.5. InMAP can be trained to run for any spatial and temporal domain given the availability of appropriate simulation output from a comprehensive model. The InMAP model source code and input data are freely available online under an open-source license.« less

InMAP: A model for air pollution interventions

PubMed Central

Hill, Jason D.; Marshall, Julian D.

2017-01-01

Mechanistic air pollution modeling is essential in air quality management, yet the extensive expertise and computational resources required to run most models prevent their use in many situations where their results would be useful. Here, we present InMAP (Intervention Model for Air Pollution), which offers an alternative to comprehensive air quality models for estimating the air pollution health impacts of emission reductions and other potential interventions. InMAP estimates annual-average changes in primary and secondary fine particle (PM2.5) concentrations—the air pollution outcome generally causing the largest monetized health damages–attributable to annual changes in precursor emissions. InMAP leverages pre-processed physical and chemical information from the output of a state-of-the-science chemical transport model and a variable spatial resolution computational grid to perform simulations that are several orders of magnitude less computationally intensive than comprehensive model simulations. In comparisons run here, InMAP recreates comprehensive model predictions of changes in total PM2.5 concentrations with population-weighted mean fractional bias (MFB) of −17% and population-weighted R2 = 0.90. Although InMAP is not specifically designed to reproduce total observed concentrations, it is able to do so within published air quality model performance criteria for total PM2.5. Potential uses of InMAP include studying exposure, health, and environmental justice impacts of potential shifts in emissions for annual-average PM2.5. InMAP can be trained to run for any spatial and temporal domain given the availability of appropriate simulation output from a comprehensive model. The InMAP model source code and input data are freely available online under an open-source license. PMID:28423049

InMAP: A model for air pollution interventions

DOE Office of Scientific and Technical Information (OSTI.GOV)

Tessum, Christopher W.; Hill, Jason D.; Marshall, Julian D.

Mechanistic air pollution modeling is essential in air quality management, yet the extensive expertise and computational resources required to run most models prevent their use in many situations where their results would be useful. We present InMAP (Intervention Model for Air Pollution), which offers an alternative to comprehensive air quality models for estimating the air pollution health impacts of emission reductions and other potential interventions. InMAP estimates annual-average changes in primary and secondary fine particle (PM2.5) concentrations—the air pollution outcome generally causing the largest monetized health damages–attributable to annual changes in precursor emissions. InMAP leverages pre-processed physical and chemical informationmore » from the output of a state-of-the-science chemical transport model and a variable spatial resolution computational grid to perform simulations that are several orders of magnitude less computationally intensive than comprehensive model simulations. In comparisons we run, InMAP recreates comprehensive model predictions of changes in total PM2.5 concentrations with population-weighted mean fractional bias (MFB) of -17% and population-weighted R2 = 0.90. Although InMAP is not specifically designed to reproduce total observed concentrations, it is able to do so within published air quality model performance criteria for total PM2.5. Potential uses of InMAP include studying exposure, health, and environmental justice impacts of potential shifts in emissions for annual-average PM2.5. InMAP can be trained to run for any spatial and temporal domain given the availability of appropriate simulation output from a comprehensive model. The InMAP model source code and input data are freely available online under an open-source license.« less

Photochemical Grid Modelling Study to Assess Potential Air Quality Impacts Associated with Energy Development in Colorado and Northern New Mexico.

NASA Astrophysics Data System (ADS)

Parker, L. K.; Morris, R. E.; Zapert, J.; Cook, F.; Koo, B.; Rasmussen, D.; Jung, J.; Grant, J.; Johnson, J.; Shah, T.; Pavlovic, T.

2015-12-01

The Colorado Air Resource Management Modeling Study (CARMMS) was funded by the Bureau of Land Management (BLM) to predict the impacts from future federal and non-federal energy development in Colorado and Northern New Mexico. The study used the Comprehensive Air Quality Model with extensions (CAMx) photochemical grid model (PGM) to quantify potential impacts from energy development from BLM field office planning areas. CAMx source apportionment technology was used to track the impacts from multiple (14) different emissions source regions (i.e. field office areas) within one simulation, as well as to assess the cumulative impact of emissions from all source regions combined. The energy development emissions estimates were for the year 2021 for three different development scenarios: (1) low; (2) high; (3) high with emissions mitigation. Impacts on air quality (AQ) including ozone, PM2.5, PM10, NO2, SO2, and air quality related values (AQRVs) such as atmospheric deposition, regional haze and changes in Acid Neutralizing Capacity (ANC) of lakes were quantified, and compared to establish threshold levels. In this presentation, we present a brief summary of the how the emission scenarios were developed, we compare the emission totals for each scenario, and then focus on the ozone impacts for each scenario to assess: (1). the difference in potential ozone impacts under the different development scenarios and (2). to establish the sensitivity of the ozone impacts to different emissions levels. Region-wide ozone impacts will be presented as well as impacts at specific locations with ozone monitors.

Air-quality implications of widespread adoption of cool roofs on ozone and particulate matter in southern California

NASA Astrophysics Data System (ADS)

Ban-Weiss, G. A.; Lee, S. M.; Katzenstein, A. S.; Carreras-Sospedra, M.; Zhang, X.; Farina, S.; Vahmani, P.; Fine, P.; Epstein, S. A.

2017-12-01

The installation of roofing materials with increased solar reflectance (i.e., "cool roofs") can mitigate the urban heat island effect and reduce energy use. In addition, meteorological changes, along with the possibility of enhanced UV reflection from these surfaces, can have complex impacts on ozone and PM2.5 concentrations. We aim to evaluate the air-quality impacts of widespread cool-roof installations prescribed by building energy efficiency standards within the heavily populated and polluted South Coast Air Basin (SoCAB) in Southern California. Development of a comprehensive rooftop area database and evaluation of spectral reflectance measurements of roofing materials allows us to predict potential future changes in solar and UV reflectance for simulations using the Weather Research Forecast and Community Multiscale Air Quality (CMAQ) models. Meteorological simulations indicate a decrease in daily maximum temperatures, daily maximum boundary layer heights, and ventilation coefficients throughout the SoCAB upon widespread installation of cool roofs. CMAQ simulations show significant increases in PM2.5 concentrations and policy-relevant design values. Changes in 8-h ozone concentrations depend on the potential change in UV reflectance, ranging from a decrease in population-weighted concentrations when UV reflectance remains unchanged to an increase when changes in UV reflectance are at an upper bound. However, 8-h policy-relevant ozone design values increase in all cases. Although the other benefits of cool roofs could outweigh small air-quality penalties, UV reflectance standards for cool roofing materials could mitigate these negative consequences. Results of this study motivate the careful consideration of future rooftop and pavement solar reflectance modification policies.

A mechanistic modeling system for estimating large scale emissions and transport of pollen and co-allergens

PubMed Central

Efstathiou, Christos; Isukapalli, Sastry

2011-01-01

Allergic airway diseases represent a complex health problem which can be exacerbated by the synergistic action of pollen particles and air pollutants such as ozone. Understanding human exposures to aeroallergens requires accurate estimates of the spatial distribution of airborne pollen levels as well as of various air pollutants at different times. However, currently there are no established methods for estimating allergenic pollen emissions and concentrations over large geographic areas such as the United States. A mechanistic modeling system for describing pollen emissions and transport over extensive domains has been developed by adapting components of existing regional scale air quality models and vegetation databases. First, components of the Biogenic Emissions Inventory System (BEIS) were adapted to predict pollen emission patterns. Subsequently, the transport module of the Community Multiscale Air Quality (CMAQ) modeling system was modified to incorporate description of pollen transport. The combined model, CMAQ-pollen, allows for simultaneous prediction of multiple air pollutants and pollen levels in a single model simulation, and uses consistent assumptions related to the transport of multiple chemicals and pollen species. Application case studies for evaluating the combined modeling system included the simulation of birch and ragweed pollen levels for the year 2002, during their corresponding peak pollination periods (April for birch and September for ragweed). The model simulations were driven by previously evaluated meteorological model outputs and emissions inventories for the eastern United States for the simulation period. A semi-quantitative evaluation of CMAQ-pollen was performed using tree and ragweed pollen counts in Newark, NJ for the same time periods. The peak birch pollen concentrations were predicted to occur within two days of the peak measurements, while the temporal patterns closely followed the measured profiles of overall tree pollen. For the case of ragweed pollen, the model was able to capture the patterns observed during September 2002, but did not predict an early peak; this can be associated with a wider species pollination window and inadequate spatial information in current land cover databases. An additional sensitivity simulation was performed to comparatively evaluate the dispersion patterns predicted by CMAQ-pollen with those predicted by the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model, which is used extensively in aerobiological studies. The CMAQ estimated concentration plumes matched the equivalent pollen scenario modeled with HYSPLIT. The novel pollen modeling approach presented here allows simultaneous estimation of multiple airborne allergens and other air pollutants, and is being developed as a central component of an integrated population exposure modeling system, the Modeling Environment for Total Risk studies (MENTOR) for multiple, co-occurring contaminants that include aeroallergens and irritants. PMID:21516207

A mechanistic modeling system for estimating large-scale emissions and transport of pollen and co-allergens

NASA Astrophysics Data System (ADS)

Efstathiou, Christos; Isukapalli, Sastry; Georgopoulos, Panos

2011-04-01

Allergic airway diseases represent a complex health problem which can be exacerbated by the synergistic action of pollen particles and air pollutants such as ozone. Understanding human exposures to aeroallergens requires accurate estimates of the spatial distribution of airborne pollen levels as well as of various air pollutants at different times. However, currently there are no established methods for estimating allergenic pollen emissions and concentrations over large geographic areas such as the United States. A mechanistic modeling system for describing pollen emissions and transport over extensive domains has been developed by adapting components of existing regional scale air quality models and vegetation databases. First, components of the Biogenic Emissions Inventory System (BEIS) were adapted to predict pollen emission patterns. Subsequently, the transport module of the Community Multiscale Air Quality (CMAQ) modeling system was modified to incorporate description of pollen transport. The combined model, CMAQ-pollen, allows for simultaneous prediction of multiple air pollutants and pollen levels in a single model simulation, and uses consistent assumptions related to the transport of multiple chemicals and pollen species. Application case studies for evaluating the combined modeling system included the simulation of birch and ragweed pollen levels for the year 2002, during their corresponding peak pollination periods (April for birch and September for ragweed). The model simulations were driven by previously evaluated meteorological model outputs and emissions inventories for the eastern United States for the simulation period. A semi-quantitative evaluation of CMAQ-pollen was performed using tree and ragweed pollen counts in Newark, NJ for the same time periods. The peak birch pollen concentrations were predicted to occur within two days of the peak measurements, while the temporal patterns closely followed the measured profiles of overall tree pollen. For the case of ragweed pollen, the model was able to capture the patterns observed during September 2002, but did not predict an early peak; this can be associated with a wider species pollination window and inadequate spatial information in current land cover databases. An additional sensitivity simulation was performed to comparatively evaluate the dispersion patterns predicted by CMAQ-pollen with those predicted by the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model, which is used extensively in aerobiological studies. The CMAQ estimated concentration plumes matched the equivalent pollen scenario modeled with HYSPLIT. The novel pollen modeling approach presented here allows simultaneous estimation of multiple airborne allergens and other air pollutants, and is being developed as a central component of an integrated population exposure modeling system, the Modeling Environment for Total Risk studies (MENTOR) for multiple, co-occurring contaminants that include aeroallergens and irritants.

Comparison of LOPES measurements with CoREAS and REAS 3.11 simulations

NASA Astrophysics Data System (ADS)

Ludwig, M.; Apel, W. D.; Arteaga-Velázquez, J. C.; Bähren, L.; Bekk, K.; Bertaina, M.; Biermann, P. L.; Blümer, J.; Bozdog, H.; Brancus, I. M.; Chiavassa, A.; Daumiller, K.; de Souza, V.; Di Pierro, F.; Doll, P.; Engel, R.; Falcke, H.; Fuchs, B.; Fuhrmann, D.; Gemmeke, H.; Grupen, C.; Haug, M.; Haungs, A.; Heck, D.; Hörandel, J. R.; Horneffer, A.; Huber, D.; Huege, T.; Isar, P. G.; Kampert, K.-H.; Kang, D.; Krömer, O.; Kuijpers, J.; Link, K.; Łuczak, P.; Mathes, H. J.; Melissas, M.; Morello, C.; Oehlschläger, J.; Palmieri, N.; Pierog, T.; Rautenberg, J.; Rebel, H.; Roth, M.; Rühle, C.; Saftoiu, A.; Schieler, H.; Schmidt, A.; Schröder, F. G.; Sima, O.; Toma, G.; Trinchero, G. C.; Weindl, A.; Wochele, J.; Zabierowski, J.; Zensus, J. A.

2013-05-01

In the previous years, LOPES emerged as a very successful experiment measuring the radio emission from air showers in the MHz frequency range. In parallel, the theoretical description of radio emission was developed further and REAS became a widely used simulation Monte Carlo code. REAS 3 as well as CoREAS are based on the endpoint formalism, i.e. they calculate the emission of the air-shower without assuming specific emission mechanisms. While REAS 3 is based on histograms derived from CORSIKA simulations, CoREAS is directly implemented into CORSIKA without loss of information due to histogramming of the particle distributions. In contrast to the earlier versions of REAS, the newest version REAS 3.11 and CoREAS take into account a realistic atmospheric refractive index. To improve the understanding of the emission processes and judge the quality of the simulations, we compare their predictions with high-quality events measured by LOPES. We present results concerning the lateral distribution measured with 30 east-west aligned LOPES antennas. Only the simulation codes including the refractive index (REAS 3.11 and CoREAS) are able to reproduce the slope of measured lateral distributions, but REAS 3.0 predicts too steep lateral distributions, and does not predict rising lateral distributions as seen in a few LOPES events. Moreover, REAS 3.11 predicts an absolute amplitude compatible with the LOPES measurements.

Source apportionment of visual impairment during the California regional PM 10/PM 2.5 air quality study

NASA Astrophysics Data System (ADS)

Chen, Jianjun; Ying, Qi; Kleeman, Michael J.

2009-12-01

Gases and particulate matter predictions from the UCD/CIT air quality model were used in a visibility model to predict source contributions to visual impairment in the San Joaquin Valley (SJV), the southern portion of California's Central Valley, during December 2000 and January 2001. Within the SJV, daytime (0800-1700 PST) light extinction was dominated by scattering associated with airborne particles. Measured daytime particle scattering coefficients were compared to predicted values at approximately 40 locations across the SJV after correction for the increased temperature and decreased relative humidity produced by "smart heaters" placed upstream of nephelometers. Mean fractional bias and mean fractional error were -0.22 and 0.65, respectively, indicating reasonable agreement between model predictions and measurements. Particulate water, nitrate, organic matter, and ammonium were the major particulate species contributing to light scattering in the SJV. Daytime light extinction in the SJV averaged between December 25, 2000 and January 7, 2001 was mainly associated with animal ammonia sources (28%), diesel engines (18%), catalyst gasoline engines (9%), other anthropogenic sources (9%), and wood smoke (7%) with initial and boundary conditions accounting for 13%. The source apportionment results from this study apply to wintertime conditions when airborne particulate matter concentrations are typically at their annual maximum. Further study would be required to quantify source contributions to light extinction in other seasons.

Bayesian maximum entropy integration of ozone observations and model predictions: an application for attainment demonstration in North Carolina.

PubMed

de Nazelle, Audrey; Arunachalam, Saravanan; Serre, Marc L

2010-08-01

States in the USA are required to demonstrate future compliance of criteria air pollutant standards by using both air quality monitors and model outputs. In the case of ozone, the demonstration tests aim at relying heavily on measured values, due to their perceived objectivity and enforceable quality. Weight given to numerical models is diminished by integrating them in the calculations only in a relative sense. For unmonitored locations, the EPA has suggested the use of a spatial interpolation technique to assign current values. We demonstrate that this approach may lead to erroneous assignments of nonattainment and may make it difficult for States to establish future compliance. We propose a method that combines different sources of information to map air pollution, using the Bayesian Maximum Entropy (BME) Framework. The approach gives precedence to measured values and integrates modeled data as a function of model performance. We demonstrate this approach in North Carolina, using the State's ozone monitoring network in combination with outputs from the Multiscale Air Quality Simulation Platform (MAQSIP) modeling system. We show that the BME data integration approach, compared to a spatial interpolation of measured data, improves the accuracy and the precision of ozone estimations across the state.

Update of NOx emission temporal profiles using CMAQ-HDDM

NASA Astrophysics Data System (ADS)

Bae, C.; Lee, J. B.; Kim, H. C.; Kim, B. U.; Kim, S.

2017-12-01

This study demonstrates the impact of revised temporal profiles of NOx emissions on air quality simulations in the Seoul Metropolitan Area (SMA), South Korea. Air pollutants such as ozone and nitrogen oxides can be harmful to the human body even with short-term exposure. Since most of air quality models use predefined temporal profiles which are often outdated or taken from different chemical environment, providing accurate temporal variation of emissions are challenging in prediction of correct local air quality. Considering secondary formation of pollutants are important in mega cities and temporal variations of emissions are not coincident with those of resultant concentrations, we utilized CMAQ-HDDM to link emissions and consequential concentrations from different time steps. Base simulations were conducted using WRF, SMOKE, and CMAQ modeling frame using CREATE 2015 and CAPSS 2013 emissions inventories for East Asia and South Korea, respectively. With current modeling system, modeled NOx concentrations underestimate 4% in the daytime (10-16 LST), but overestimate 30% in the nighttime during May to August 2015. Applying revised temporal profiles based on HDDM sensitivities, model performance was improved significantly. We conclude that the proposed temporal allocation method can be useful to reduce the model-observation discrepancies when the activity data for emission sources are difficult to obtain with a bottom-up approach.

78 FR 63934 - Approval of Air Quality Implementation Plans; California; El Dorado County Air Quality Management...

Federal Register 2010, 2011, 2012, 2013, 2014

2013-10-25

...] Approval of Air Quality Implementation Plans; California; El Dorado County Air Quality Management District... California for the El Dorado County Air Quality Management District (EDAQMD) portion of the California SIP... 24, 1987 Federal Register, May 25, 1988, U.S. EPA, Air Quality Management Division, Office of Air...

Local air gap thickness and contact area models for realistic simulation of human thermo-physiological response

NASA Astrophysics Data System (ADS)

Psikuta, Agnes; Mert, Emel; Annaheim, Simon; Rossi, René M.

2018-02-01

To evaluate the quality of new energy-saving and performance-supporting building and urban settings, the thermal sensation and comfort models are often used. The accuracy of these models is related to accurate prediction of the human thermo-physiological response that, in turn, is highly sensitive to the local effect of clothing. This study aimed at the development of an empirical regression model of the air gap thickness and the contact area in clothing to accurately simulate human thermal and perceptual response. The statistical model predicted reliably both parameters for 14 body regions based on the clothing ease allowances. The effect of the standard error in air gap prediction on the thermo-physiological response was lower than the differences between healthy humans. It was demonstrated that currently used assumptions and methods for determination of the air gap thickness can produce a substantial error for all global, mean, and local physiological parameters, and hence, lead to false estimation of the resultant physiological state of the human body, thermal sensation, and comfort. Thus, this model may help researchers to strive for improvement of human thermal comfort, health, productivity, safety, and overall sense of well-being with simultaneous reduction of energy consumption and costs in built environment.

Air quality and ventilation fan control based on aerosol measurement in the bi-directional undersea Bømlafjord tunnel.

PubMed

Indrehus, Oddny; Aralt, Tor Tybring

2005-04-01

Aerosol, NO and CO concentration, temperature, air humidity, air flow and number of running ventilation fans were measured by continuous analysers every minute for a whole week for six different one-week periods spread over ten months in 2001 and 2002 at measuring stations in the 7860 m long tunnel. The ventilation control system was mainly based on aerosol measurements taken by optical scatter sensors. The ventilation turned out to be satisfactory according to Norwegian air quality standards for road tunnels; however, there was some uncertainty concerning the NO2 levels. The air humidity and temperature inside the tunnel were highly influenced by the outside metrological conditions. Statistical models for NO concentration were developed and tested; correlations between predicted and measured NO were 0.81 for a partial least squares regression (PLS1) model based on CO and aerosol, and 0.77 for a linear regression model based only on aerosol. Hence, the ventilation control system should not solely be based on aerosol measurements. Since NO2 is the hazardous polluter, modelling NO2 concentration rather than NO should be preferred in any further optimising of the ventilation control.

Application of an integrated Weather Research and Forecasting (WRF)/CALPUFF modeling tool for source apportionment of atmospheric pollutants for air quality management: A case study in the urban area of Benxi, China.

PubMed

Wu, Hao; Zhang, Yan; Yu, Qi; Ma, Weichun

2018-04-01

In this study, the authors endeavored to develop an effective framework for improving local urban air quality on meso-micro scales in cities in China that are experiencing rapid urbanization. Within this framework, the integrated Weather Research and Forecasting (WRF)/CALPUFF modeling system was applied to simulate the concentration distributions of typical pollutants (particulate matter with an aerodynamic diameter <10 μm [PM 10 ], sulfur dioxide [SO 2 ], and nitrogen oxides [NO x ]) in the urban area of Benxi. Statistical analyses were performed to verify the credibility of this simulation, including the meteorological fields and concentration fields. The sources were then categorized using two different classification methods (the district-based and type-based methods), and the contributions to the pollutant concentrations from each source category were computed to provide a basis for appropriate control measures. The statistical indexes showed that CALMET had sufficient ability to predict the meteorological conditions, such as the wind fields and temperatures, which provided meteorological data for the subsequent CALPUFF run. The simulated concentrations from CALPUFF showed considerable agreement with the observed values but were generally underestimated. The spatial-temporal concentration pattern revealed that the maximum concentrations tended to appear in the urban centers and during the winter. In terms of their contributions to pollutant concentrations, the districts of Xihu, Pingshan, and Mingshan all affected the urban air quality to different degrees. According to the type-based classification, which categorized the pollution sources as belonging to the Bengang Group, large point sources, small point sources, and area sources, the source apportionment showed that the Bengang Group, the large point sources, and the area sources had considerable impacts on urban air quality. Finally, combined with the industrial characteristics, detailed control measures were proposed with which local policy makers could improve the urban air quality in Benxi. In summary, the results of this study showed that this framework has credibility for effectively improving urban air quality, based on the source apportionment of atmospheric pollutants. The authors endeavored to build up an effective framework based on the integrated WRF/CALPUFF to improve the air quality in many cities on meso-micro scales in China. Via this framework, the integrated modeling tool is accurately used to study the characteristics of meteorological fields, concentration fields, and source apportionments of pollutants in target area. The impacts of classified sources on air quality together with the industrial characteristics can provide more effective control measures for improving air quality. Through the case study, the technical framework developed in this study, particularly the source apportionment, could provide important data and technical support for policy makers to assess air pollution on the scale of a city in China or even the world.

76 FR 44535 - Revisions to the California State Implementation Plan, Northern Sierra Air Quality Management...

Federal Register 2010, 2011, 2012, 2013, 2014

2011-07-26

... the California State Implementation Plan, Northern Sierra Air Quality Management District, Sacramento Metropolitan Air Quality Management District, and South Coast Air Quality Management District AGENCY... the Northern Sierra Air Quality Management District (NSAQMD), Sacramento Metropolitan Air Quality...

Toward an integrated quasi-operational air quality analysis and prediction system for South America

NASA Astrophysics Data System (ADS)

Hoshyaripour, Gholam Ali; Brasseur, Guy; Petersen, Katinka; Bouarar, Idiir; Andrade, Maria de Fatima

2015-04-01

Recent industrialization and urbanization in South America (SA) have notably exacerbated the air pollution with adverse impacts on human health and socio-economic systems. Consequently, there is a strong demand for developing ever-better assessment mechanisms to monitor the air quality at different temporal and spatial scales and minimize its damages. Based on previous achievements (e.g., MACC project in Europe and PANDA project in East Asia) we aim to design and implement an integrated system to monitor, analyze and forecast the air quality in SA along with its impacts upon public health and agriculture. An initiative will be established to combine observations (both satellite and in-situ) with advanced numerical models in order to provide a robust scientific basis for short- and long-term decision-making concerning air quality issues in SA countries. The main objectives of the project are defined as 3E: Enhancement of the air quality monitoring system through coupling models and observations, Elaboration of comprehensive indicators and assessment tools to support policy-making, Establishment of efficient information-exchange platforms to facilitate communication among scientists, authorities, stockholders and the public. Here we present the results of the initial stage, where a coarse resolution (50×50 km) set up of Weather Research and Forecast model with Chemistry (WRF-Chem) is used to simulate the air quality in SA considering anthropogenic, biomass-burning (based on MACCity, FINN inventories, respectively) and biogenic emissions (using MEGAN model). According to the availability of the observation data for Metropolitan Area of São Paulo, August 2012 is selected as the simulation period. Nested domains with higher resolution (15×15 km) are also embedded within the parent domain over the megacities (Sao Paolo and Rio de Janeiro in Brazil and Buenos Aires in Argentina), which account for the major anthropogenic emission sources located along coastal regions of the continent. Fire and biogenic emissions on the other hand mainly take place within the inner parts of the continent in for e.g. Amazon basin and sugarcane in Sao Paulo State. Contributions of these emission sources in reactive gases (e.g., CO, O3, NOx) and particulate matter concentrations are quantified. Next step is to examine different emission inventories and observation data to find an optimal description for the atmospheric composition in SA.

Sound quality evaluation of air conditioning sound rating metric

NASA Astrophysics Data System (ADS)

Hodgdon, Kathleen K.; Peters, Jonathan A.; Burkhardt, Russell C.; Atchley, Anthony A.; Blood, Ingrid M.

2003-10-01

A product's success can depend on its acoustic signature as much as on the product's performance. The consumer's perception can strongly influence their satisfaction with and confidence in the product. A metric that can rate the content of the spectrum, and predict its consumer preference, is a valuable tool for manufacturers. The current method of assessing acoustic signatures from residential air conditioning units is defined in the Air Conditioning and Refrigeration Institute (ARI 270) 1995 Standard for Sound Rating of Outdoor Unitary Equipment. The ARI 270 metric, and modified versions of that metric, were implemented in software with the flexibility to modify the features applied. Numerous product signatures were analyzed to generate a set of synthesized spectra that targeted spectral configurations that challenged the metric's abilities. A subjective jury evaluation was conducted to establish the consumer preference for those spectra. Statistical correlations were conducted to assess the degree of relationship between the subjective preferences and the various metric calculations. Recommendations were made for modifications to improve the current metric's ability to predict subjective preference. [Research supported by the Air Conditioning and Refrigeration Institute.

Sensitivity of Short-Term Weather Forecasts to Assimilated AIRS Data: Implications for NPOESS Applications

NASA Technical Reports Server (NTRS)

Zavodsky, Bradley; McCarty, Will; Chou, Shih-Hung; Jedlovec, Gary

2009-01-01

The Atmospheric Infrared Sounder (AIRS) is acting as a heritage and risk reduction instrument for the Cross-track lnfrared Sounder (CrIS) to be flown aboard the NPP and NPOESS satellites. The hyperspectral nature of AIRS and CrIS provides high-quality soundings that, along with their asynoptic observation time over North America, make them attractive sources to fill the spatial and temporal data voids in upper air temperature and moisture measurements for use in data assimilation and numerical weather prediction. Observations from AlRS can be assimilated either as direct radiances or retrieved thermodynamic profiles, and the Short-Term Prediction Research and Transition (SPORT) Center at NASA's Marshall Space Flight Center has used both data types to improve short-term (0-48h), regional forecasts. The purpose of this paper is to share SPORT'S experiences using AlRS radiances and retrieved profiles in regional data assimilation activities by showing that proper handling of issues-including cloud contamination and land emissivity characterization-are necessary to produce optimal analyses and forecasts.

Sensitivity of local air quality to the interplay between small- and large-scale circulations: a large-eddy simulation study

NASA Astrophysics Data System (ADS)

Wolf-Grosse, Tobias; Esau, Igor; Reuder, Joachim

2017-06-01

Street-level urban air pollution is a challenging concern for modern urban societies. Pollution dispersion models assume that the concentrations decrease monotonically with raising wind speed. This convenient assumption breaks down when applied to flows with local recirculations such as those found in topographically complex coastal areas. This study looks at a practically important and sufficiently common case of air pollution in a coastal valley city. Here, the observed concentrations are determined by the interaction between large-scale topographically forced and local-scale breeze-like recirculations. Analysis of a long observational dataset in Bergen, Norway, revealed that the most extreme cases of recurring wintertime air pollution episodes were accompanied by increased large-scale wind speeds above the valley. Contrary to the theoretical assumption and intuitive expectations, the maximum NO2 concentrations were not found for the lowest 10 m ERA-Interim wind speeds but in situations with wind speeds of 3 m s-1. To explain this phenomenon, we investigated empirical relationships between the large-scale forcing and the local wind and air quality parameters. We conducted 16 large-eddy simulation (LES) experiments with the Parallelised Large-Eddy Simulation Model (PALM) for atmospheric and oceanic flows. The LES accounted for the realistic relief and coastal configuration as well as for the large-scale forcing and local surface condition heterogeneity in Bergen. They revealed that emerging local breeze-like circulations strongly enhance the urban ventilation and dispersion of the air pollutants in situations with weak large-scale winds. Slightly stronger large-scale winds, however, can counteract these local recirculations, leading to enhanced surface air stagnation. Furthermore, this study looks at the concrete impact of the relative configuration of warmer water bodies in the city and the major transport corridor. We found that a relatively small local water body acted as a barrier for the horizontal transport of air pollutants from the largest street in the valley and along the valley bottom, transporting them vertically instead and hence diluting them. We found that the stable stratification accumulates the street-level pollution from the transport corridor in shallow air pockets near the surface. The polluted air pockets are transported by the local recirculations to other less polluted areas with only slow dilution. This combination of relatively long distance and complex transport paths together with weak dispersion is not sufficiently resolved in classical air pollution models. The findings have important implications for the air quality predictions over urban areas. Any prediction not resolving these, or similar local dynamic features, might not be able to correctly simulate the dispersion of pollutants in cities.

Ambient Air Quality Data Inventory

EPA Pesticide Factsheets

The Office of Air and Radiation's (OAR) Ambient Air Quality Data (Current) contains ambient air pollution data collected by EPA, other federal agencies, as well as state, local, and tribal air pollution control agencies. Its component data sets have been collected over the years from approximately 10,000 monitoring sites, of which approximately 5,000 are currently active. OAR's Office of Air Quality Planning and Standards (OAQPS) and other internal and external users, rely on this data to assess air quality, assist in Attainment/Non-Attainment designations, evaluate State Implementation Plans for Non-Attainment Areas, perform modeling for permit review analysis, and other air quality management functions. Air quality information is also used to prepare reports for Congress as mandated by the Clean Air Act. This data covers air quality data collected after 1980, when the Clean Air Act requirements for monitoring were significantly modified. Air quality data from the Agency's early years (1970s) remains available (see OAR PRIMARY DATA ASSET: Ambient Air Quality Data -- Historical), but because of technical and definitional differences the two data assets are not directly comparable. The Clean Air Act of 1970 provided initial authority for monitoring air quality for Conventional Air Pollutants (CAPs) for which EPA has promulgated National Ambient Air Quality Standards (NAAQS). Requirements for monitoring visibility-related parameters were added in 1977. Requiremen

Development of a multi-ensemble Prediction Model for China

NASA Astrophysics Data System (ADS)

Brasseur, G. P.; Bouarar, I.; Petersen, A. K.

2016-12-01

As part of the EU-sponsored Panda and MarcoPolo Projects, a multi-model prediction system including 7 models has been developed. Most regional models use global air quality predictions provided by the Copernicus Atmospheric Monitoring Service and downscale the forecast at relatively high spatial resolution in eastern China. The paper will describe the forecast system and show examples of forecasts produced for several Chinese urban areas and displayed on a web site developed by the Dutch Meteorological service. A discussion on the accuracy of the predictions based on a detailed validation process using surface measurements from the Chinese monitoring network will be presented.

78 FR 30770 - Approval and Promulgation of Air Quality Implementation Plans; Illinois; Air Quality Standards...

Federal Register 2010, 2011, 2012, 2013, 2014

2013-05-23

... Promulgation of Air Quality Implementation Plans; Illinois; Air Quality Standards Revision AGENCY... Illinois state implementation plan (SIP) to reflect current National Ambient Air Quality Standards (NAAQS... Implementation Plan at 35 Illinois Administrative Code part 243, which updates National Ambient Air Quality...

75 FR 65572 - Approval and Promulgation of Air Quality Implementation Plans; Ohio; Ohio Ambient Air Quality...

Federal Register 2010, 2011, 2012, 2013, 2014

2010-10-26

... Promulgation of Air Quality Implementation Plans; Ohio; Ohio Ambient Air Quality Standards AGENCY... Ohio Administrative Code (OAC) relating to the consolidation of Ohio's Ambient Air Quality Standards... apply to Ohio's SIP. Incorporating the air quality standards into Ohio's SIP helps assure that...

Applications of MODIS satellite data and products for monitoring air quality in the state of Texas

NASA Astrophysics Data System (ADS)

Hutchison, Keith D.

The Center for Space Research (CSR), in conjunction with the Monitoring Operations Division (MOD) of the Texas Commission on Environmental Quality (TCEQ), is evaluating the use of remotely sensed satellite data to assist in monitoring and predicting air quality in Texas. The challenges of meeting air quality standards established by the US Environmental Protection Agency (US EPA) are impacted by the transport of pollution into Texas that originates from outside our borders and are cumulative with those generated by local sources. In an attempt to quantify the concentrations of all pollution sources, MOD has installed ground-based monitoring stations in rural regions along the Texas geographic boundaries including the Gulf coast, as well as urban regions that are the predominant sources of domestic pollution. However, analysis of time-lapse GOES satellite imagery at MOD, clearly demonstrates the shortcomings of using only ground-based observations for monitoring air quality across Texas. These shortcomings include the vastness of State borders, that can only be monitored with a large number of ground-based sensors, and gradients in pollution concentration that depend upon the location of the point source, the meteorology governing its transport to Texas, and its diffusion across the region. With the launch of NASA's MODerate resolution Imaging Spectroradiometer (MODIS), the transport of aerosol-borne pollutants can now be monitored over land and ocean surfaces. Thus, CSR and MOD personnel have applied MODIS data to several classes of pollution that routinely impact Texas air quality. Results demonstrate MODIS data and products can detect and track the migration of pollutants. This paper presents one case study in which continental haze from the northeast moved into the region and subsequently required health advisories to be issued for 150 counties in Texas. It is concluded that MODIS provides the basis for developing advanced data products that will, when used in conjunction with ground-based observations, create a cost-effective and accurate pollution monitoring system for the entire state of Texas.

How Clean is your Local Air? Here's an app for that

NASA Astrophysics Data System (ADS)

Maskey, M.; Yang, E.; Christopher, S. A.; Keiser, K.; Nair, U. S.; Graves, S. J.

2011-12-01

Air quality is a vital element of our environment. Accurate and localized air quality information is critical for characterizing environmental impacts at the local and regional levels. Advances in location-aware handheld devices and air quality modeling have enabled a group of UAHuntsville scientists to develop a mobile app, LocalAQI, that informs users of current conditions and forecasts of up to twenty-four hours, of air quality indices. The air quality index is based on Community Multiscale Air Quality Modeling System (CMAQ). UAHuntsville scientists have used satellite remote sensing products as inputs to CMAQ, resulting in forecast guidance for particulate matter air quality. The CMAQ output is processed to compute a standardized air quality index. Currently, the air quality index is available for the eastern half of the United States. LocalAQI consists of two main views: air quality index view and map view. The air quality index view displays current air quality for the zip code of a location of interest. Air quality index value is translated into a color-coded advisory system. In addition, users are able to cycle through available hourly forecasts for a location. This location-aware app defaults to the current air quality of user's location. The map view displays color-coded air quality information for the eastern US with an ability to animate through the available forecasts. The app is developed using a cross-platform native application development tool, appcelerator; hence LocalAQI is available for iOS and Android-based phones and pads.

Evaluation of the Impact of AIRS Radiance and Profile Data Assimilation in Partly Cloudy Regions

NASA Technical Reports Server (NTRS)

Zavodsky, Bradley; Srikishen, Jayanthi; Jedlovec, Gary

2013-01-01

Improvements to global and regional numerical weather prediction have been demonstrated through assimilation of data from NASA s Atmospheric Infrared Sounder (AIRS). Current operational data assimilation systems use AIRS radiances, but impact on regional forecasts has been much smaller than for global forecasts. Retrieved profiles from AIRS contain much of the information that is contained in the radiances and may be able to reveal reasons for this reduced impact. Assimilating AIRS retrieved profiles in an identical analysis configuration to the radiances, tracking the quantity and quality of the assimilated data in each technique, and examining analysis increments and forecast impact from each data type can yield clues as to the reasons for the reduced impact. By doing this with regional scale models individual synoptic features (and the impact of AIRS on these features) can be more easily tracked. This project examines the assimilation of hyperspectral sounder data used in operational numerical weather prediction by comparing operational techniques used for AIRS radiances and research techniques used for AIRS retrieved profiles. Parallel versions of a configuration of the Weather Research and Forecasting (WRF) model with Gridpoint Statistical Interpolation (GSI) are run to examine the impact AIRS radiances and retrieved profiles. Statistical evaluation of a long-term series of forecast runs will be compared along with preliminary results of in-depth investigations for select case comparing the analysis increments in partly cloudy regions and short-term forecast impacts.

Evaluation of the Impact of Atmospheric Infrared Sounder (AIRS) Radiance and Profile Data Assimilation in Partly Cloudy Regions

NASA Technical Reports Server (NTRS)

Zavodsky, Bradley; Srikishen, Jayanthi; Jedlovec, Gary

2013-01-01

Improvements to global and regional numerical weather prediction have been demonstrated through assimilation of data from NASA s Atmospheric Infrared Sounder (AIRS). Current operational data assimilation systems use AIRS radiances, but impact on regional forecasts has been much smaller than for global forecasts. Retrieved profiles from AIRS contain much of the information that is contained in the radiances and may be able to reveal reasons for this reduced impact. Assimilating AIRS retrieved profiles in an identical analysis configuration to the radiances, tracking the quantity and quality of the assimilated data in each technique, and examining analysis increments and forecast impact from each data type can yield clues as to the reasons for the reduced impact. By doing this with regional scale models individual synoptic features (and the impact of AIRS on these features) can be more easily tracked. This project examines the assimilation of hyperspectral sounder data used in operational numerical weather prediction by comparing operational techniques used for AIRS radiances and research techniques used for AIRS retrieved profiles. Parallel versions of a configuration of the Weather Research and Forecasting (WRF) model with Gridpoint Statistical Interpolation (GSI) are run to examine the impact AIRS radiances and retrieved profiles. Statistical evaluation of 6 weeks of forecast runs will be compared along with preliminary results of in-depth investigations for select case comparing the analysis increments in partly cloudy regions and short-term forecast impacts.

DETERMINING PARTICLE EMISSION SOURCE STRENGTHS FOR COMMON RESIDENTIAL INDOOR SOURCES USING REAL-TIME MEASUREMENTS AND PIECEWISE-CONTINUOUS SOLUTIONS TO THE MASS BALANCE EQUATION

EPA Science Inventory

A variety of common activities in the home, such as smoking and cooking, generate indoor particle concentrations. Mathematical indoor air quality models permit predictions of indoor pollutant concentrations in homes, provided that parameter values such as source strengths and ...

SENSITIVITY OF OZONE AND AEROSOL PREDICTIONS TO THE TRANSPORT ALGORITHMS IN THE MODELS-3 COMMUNITY MULTI-SCALE AIR QUALITY (CMAQ) MODELING SYSTEM

EPA Science Inventory

EPA's Models-3 CMAQ system is intended to provide a community modeling paradigm that allows continuous improvement of the one-atmosphere modeling capability in a unified fashion. CMAQ's modular design promotes incorporation of several sets of science process modules representing ...

CHARACTERIZATION OF PM-10 EMISSIONS FROM ANTISKID MATERIALS APPLIED TO ICE- AND SNOW-COVERED ROADWAYS

EPA Science Inventory

The report gives results of a field program to establish a predictive model for PM-10 (particulate matter with diameters or < 10 micrometers) emission. NOTE: Several areas of the U.S. in violation of the National Ambient Air Quality Standard for PM-10 have conducted studies that ...

Ohio Environmental Education Areas.

ERIC Educational Resources Information Center

Melvin, Ruth W.

This is a guide to regional sites in Ohio which can be studied in regard to resource management; land use; the quality of air, water, soil; and reclamation. The first section of the guide includes brief descriptions of Ohio's natural features at the present time, accounts of past appearances and events, and predictions for the future. In the…

40 CFR 81.77 - Puerto Rico Air Quality Control Region.

Code of Federal Regulations, 2011 CFR

2011-07-01

... 40 Protection of Environment 17 2011-07-01 2011-07-01 false Puerto Rico Air Quality Control Region... PROGRAMS (CONTINUED) DESIGNATION OF AREAS FOR AIR QUALITY PLANNING PURPOSES Designation of Air Quality Control Regions § 81.77 Puerto Rico Air Quality Control Region. The Puerto Rico Air Quality Control Region...

40 CFR 81.76 - State of Hawaii Air Quality Control Region.

Code of Federal Regulations, 2011 CFR

2011-07-01

... 40 Protection of Environment 17 2011-07-01 2011-07-01 false State of Hawaii Air Quality Control... PROGRAMS (CONTINUED) DESIGNATION OF AREAS FOR AIR QUALITY PLANNING PURPOSES Designation of Air Quality Control Regions § 81.76 State of Hawaii Air Quality Control Region. The State of Hawaii Air Quality...

40 CFR 81.77 - Puerto Rico Air Quality Control Region.

Code of Federal Regulations, 2012 CFR

2012-07-01

... 40 Protection of Environment 18 2012-07-01 2012-07-01 false Puerto Rico Air Quality Control Region... PROGRAMS (CONTINUED) DESIGNATION OF AREAS FOR AIR QUALITY PLANNING PURPOSES Designation of Air Quality Control Regions § 81.77 Puerto Rico Air Quality Control Region. The Puerto Rico Air Quality Control Region...

40 CFR 81.76 - State of Hawaii Air Quality Control Region.

Code of Federal Regulations, 2012 CFR

2012-07-01

... 40 Protection of Environment 18 2012-07-01 2012-07-01 false State of Hawaii Air Quality Control... PROGRAMS (CONTINUED) DESIGNATION OF AREAS FOR AIR QUALITY PLANNING PURPOSES Designation of Air Quality Control Regions § 81.76 State of Hawaii Air Quality Control Region. The State of Hawaii Air Quality...

40 CFR 81.76 - State of Hawaii Air Quality Control Region.

Code of Federal Regulations, 2013 CFR

2013-07-01

... 40 Protection of Environment 18 2013-07-01 2013-07-01 false State of Hawaii Air Quality Control... PROGRAMS (CONTINUED) DESIGNATION OF AREAS FOR AIR QUALITY PLANNING PURPOSES Designation of Air Quality Control Regions § 81.76 State of Hawaii Air Quality Control Region. The State of Hawaii Air Quality...

40 CFR 81.77 - Puerto Rico Air Quality Control Region.

Code of Federal Regulations, 2013 CFR

2013-07-01

... 40 Protection of Environment 18 2013-07-01 2013-07-01 false Puerto Rico Air Quality Control Region... PROGRAMS (CONTINUED) DESIGNATION OF AREAS FOR AIR QUALITY PLANNING PURPOSES Designation of Air Quality Control Regions § 81.77 Puerto Rico Air Quality Control Region. The Puerto Rico Air Quality Control Region...

78 FR 19990 - Approval and Promulgation of Air Quality Implementation Plans; Ohio; Ohio Ambient Air Quality...

Federal Register 2010, 2011, 2012, 2013, 2014

2013-04-03

... Promulgation of Air Quality Implementation Plans; Ohio; Ohio Ambient Air Quality Standards; Correction AGENCY... approved revisions to Ohio regulations that consolidated air quality standards in a new chapter of rules... State's air quality standards into Ohio Administrative Code (OAC) 3745-25 and modifying an assortment of...

77 FR 12482 - Approval and Promulgation of Air Quality Implementation Plans; Indiana; Lead Ambient Air Quality...

Federal Register 2010, 2011, 2012, 2013, 2014

2012-03-01

... Promulgation of Air Quality Implementation Plans; Indiana; Lead Ambient Air Quality Standards AGENCY... incorporates the National Ambient Air Quality Standards (NAAQS) for Pb promulgated by EPA in 2008. DATES: This... FR 66964) and codified at 40 CFR 50.16, ``National primary and secondary ambient air quality...

40 CFR 81.77 - Puerto Rico Air Quality Control Region.

Code of Federal Regulations, 2010 CFR

2010-07-01

... 40 Protection of Environment 17 2010-07-01 2010-07-01 false Puerto Rico Air Quality Control Region... PROGRAMS (CONTINUED) DESIGNATION OF AREAS FOR AIR QUALITY PLANNING PURPOSES Designation of Air Quality Control Regions § 81.77 Puerto Rico Air Quality Control Region. The Puerto Rico Air Quality Control Region...

40 CFR 81.76 - State of Hawaii Air Quality Control Region.

Code of Federal Regulations, 2010 CFR

2010-07-01

... 40 Protection of Environment 17 2010-07-01 2010-07-01 false State of Hawaii Air Quality Control... PROGRAMS (CONTINUED) DESIGNATION OF AREAS FOR AIR QUALITY PLANNING PURPOSES Designation of Air Quality Control Regions § 81.76 State of Hawaii Air Quality Control Region. The State of Hawaii Air Quality...

Effects of Changing Emissions on Ozone and Particulates in the Northeastern United States

NASA Astrophysics Data System (ADS)

Frost, G. J.; McKeen, S.; Trainer, M.; Ryerson, T.; Holloway, J.; Brock, C.; Middlebrook, A.; Wollny, A.; Matthew, B.; Williams, E.; Lerner, B.; Fortin, T.; Sueper, D.; Parrish, D.; Fehsenfeld, F.; Peckham, S.; Grell, G.; Peltier, R.; Weber, R.; Quinn, P.; Bates, T.

2004-12-01

Emissions of nitrogen oxides (NOx) from electric power generation have decreased in recent years due to changes in burner technology and fuels used. Mobile NOx emissions assessments are less certain, since they must account for increases in vehicle miles traveled, changes in the proportion of diesel and gasoline vehicles, and more stringent controls on engines and fuels. The impact of these complicated emission changes on a particular region's air quality must be diagnosed by a combination of observation and model simulation. The New England Air Quality Study - Intercontinental Transport and Chemical Transformation 2004 (NEAQS-ITCT 2004) program provides an opportunity to test the effects of changes in emissions of NOx and other precursors on air quality in the northeastern United States. An array of ground, marine, and airborne observation platforms deployed during the study offer checks on emission inventories and air quality model simulations, like those of the Weather Research and Forecasting model coupled with online chemistry (WRF-Chem). Retrospective WRF-Chem runs are carried out with two EPA inventories, one compiled for base year 1999 and an update for 2004 incorporating projected and known changes in emissions during the past 5 years. Differences in model predictions of ozone, particulates, and other tracers using the two inventories are investigated. The inventories themselves and the model simulations are compared with the extensive observations available during NEAQS-ITCT 2004. Preliminary insights regarding the sensitivity of the model to NOx emission changes are discussed.

Modeling and Mechanisms of Intercontinental Transport of Biomass-Burning Plumes

NASA Astrophysics Data System (ADS)

Reid, J. S.; Westphal, D. L.; Christopher, S. A.; Prins, E. M.; Justice, C. O.; Richardson, K. A.; Reid, E. A.; Eck, T. F.

2003-12-01

With the aid of fire products from GOES and MODIS, the NRL Aerosol Analysis and Prediction System (NAAPS) successfully monitors and predicts the formation and transport of massive smoke plumes between the continents in near real time. The goal of this system, formed under the joint Navy, NASA, and NOAA sponsored Fire Locating and Modeling of Burning Emissions (FLAMBE) project, is to provide 5 day forecasts of large biomass burning plumes and evaluate impacts on air quality, visibility, and regional radiative balance. In this paper we discuss and compare the mechanisms of intercontinental transport from the three most important sources in the world prone to long range advection: Africa, South/Central America, and Siberia. We demonstrate how these regions impact neighboring continents. As the meteorology of these three regions are distinct, differences in transport phenomenon subsequently result, particularly with respect to vertical distribution. Specific examples will be given on prediction and the impact of Siberian and Central American smoke plumes on the United States as well as transport phenomena from Africa to Australia. We present rules of thumb for radiation and air quality impacts. We also model clear sky bias (both positive and negative) with respect to MODIS data, and show the frequency to which frontal advection of smoke plumes masks remote sensing retrievals of smoke optical depth.

A systematic review of data mining and machine learning for air pollution epidemiology.

PubMed

Bellinger, Colin; Mohomed Jabbar, Mohomed Shazan; Zaïane, Osmar; Osornio-Vargas, Alvaro

2017-11-28

Data measuring airborne pollutants, public health and environmental factors are increasingly being stored and merged. These big datasets offer great potential, but also challenge traditional epidemiological methods. This has motivated the exploration of alternative methods to make predictions, find patterns and extract information. To this end, data mining and machine learning algorithms are increasingly being applied to air pollution epidemiology. We conducted a systematic literature review on the application of data mining and machine learning methods in air pollution epidemiology. We carried out our search process in PubMed, the MEDLINE database and Google Scholar. Research articles applying data mining and machine learning methods to air pollution epidemiology were queried and reviewed. Our search queries resulted in 400 research articles. Our fine-grained analysis employed our inclusion/exclusion criteria to reduce the results to 47 articles, which we separate into three primary areas of interest: 1) source apportionment; 2) forecasting/prediction of air pollution/quality or exposure; and 3) generating hypotheses. Early applications had a preference for artificial neural networks. In more recent work, decision trees, support vector machines, k-means clustering and the APRIORI algorithm have been widely applied. Our survey shows that the majority of the research has been conducted in Europe, China and the USA, and that data mining is becoming an increasingly common tool in environmental health. For potential new directions, we have identified that deep learning and geo-spacial pattern mining are two burgeoning areas of data mining that have good potential for future applications in air pollution epidemiology. We carried out a systematic review identifying the current trends, challenges and new directions to explore in the application of data mining methods to air pollution epidemiology. This work shows that data mining is increasingly being applied in air pollution epidemiology. The potential to support air pollution epidemiology continues to grow with advancements in data mining related to temporal and geo-spacial mining, and deep learning. This is further supported by new sensors and storage mediums that enable larger, better quality data. This suggests that many more fruitful applications can be expected in the future.

A novel method to construct an air quality index based on air pollution profiles.

PubMed

Thach, Thuan-Quoc; Tsang, Hilda; Cao, Peihua; Ho, Lai-Ming

2018-01-01

Air quality indices based on the maximum of sub-indices of pollutants are easy to produce and help quantify the degree of air pollution. However, they discount the additive effects of multiple pollutants and are only sensitive to changes in highest sub-index. We propose a simple and concise method to construct an air quality index that takes into account additive effects of multiple pollutants and evaluate the extent to which this index predicts health effects. We obtained concentrations of four criteria pollutants: particulate matter with aerodynamic diameter ≤ 10μm (PM 10 ), sulphur dioxide (SO 2 ), nitrogen dioxide (NO 2 ) and ozone (O 3 ) and daily admissions to Hong Kong hospitals for cardiovascular and respiratory diseases for all ages and those 65 years or older for years 2001-2012. We derived sub-indices of the four criteria pollutants, calculated by normalizing pollutant concentrations to their respective short-term WHO Air Quality Guidelines (WHO AQG). We aggregated the sub-indices using the root-mean-power function with an optimal power to form an overall air quality index. The optimal power was determined by minimizing the sum of over- and under-estimated days. We then assessed associations between the pollution bands of the index and cardiovascular and respiratory admissions using a time-stratified case-crossover design adjusted for ambient temperature, relative humidity and influenza epidemics. Further, we conducted case-crossover analyses using the Hong Kong air quality data with the respective standards and classification of pollution bands of the China Air Quality Index (AQI), the United Kingdom Daily AQI (DAQI), and the United States Environmental Protection Agency (USEPA) AQI. The mean concentrations of PM 10 and SO 2 based on maximum 3-h mean exceeded the WHO AQG by 37% and 50%, respectively. We identified the combined condition of observed high-pollution days as either at least one pollutant > 1.5×WHO AQG or at least two pollutants > 1.0×WHO AQG to characterize the typical pollution profiles over the study period, which resulted in the optimal power=3.0. The distribution of days in different pollution bands of the index was: 5.8% for "Low" (0-50), 37.6% for "Moderate" (51-100), 31.1% for "High" (101-150), 14.7% for "Very High" (151-200), and 10.8% for "Serious" (201+). For cardiovascular and respiratory admissions, there were significant associations with the pollution bands of the index for all ages and those 65 years or older. The trends of increasing pollution bands in relation to increasing excess risks of cardiovascular and respiratory admissions were significant for the proposed index, the China AQI, the UK DAQI and the USEPA AQI (P value for test for linear trend < 0.0001), suggesting a dose-response relation. We have developed a simple and concise method to construct an air quality index that accounts for multiple pollutants to quantify air quality conditions for Hong Kong. Further developments are needed in order to support the extension of the method to other settings. Copyright © 2017 Elsevier GmbH. All rights reserved.

Air pollution response to changing weather and power plant emissions in the eastern United States

NASA Astrophysics Data System (ADS)

Bloomer, Bryan Jaye

Air pollution in the eastern United States causes human sickness and death as well as damage to crops and materials. NOX emission reduction is observed to improve air quality. Effectively reducing pollution in the future requires understanding the connections between smog, precursor emissions, weather, and climate change. Numerical models predict global warming will exacerbate smog over the next 50 years. My analysis of 21 years of CASTNET observations quantifies a climate change penalty. I calculate, for data collected prior to 2002, a climate penalty factor of ˜3.3 ppb O3/°C across the power plant dominated receptor regions in the rural, eastern U.S. Recent reductions in NOX emissions decreased the climate penalty factor to ˜2.2 ppb O3/°C. Prior to 1995, power plant emissions of CO2, SO2, and NOX were estimated with fuel sampling and analysis methods. Currently, emissions are measured with continuous monitoring equipment (CEMS) installed directly in stacks. My comparison of the two methods show CO 2 and SO2 emissions are ˜5% lower when inferred from fuel sampling; greater differences are found for NOX emissions. CEMS are the method of choice for emission inventories and commodity trading and should be the standard against which other methods are evaluated for global greenhouse gas trading policies. I used CEMS data and applied chemistry transport modeling to evaluate improvements in air quality observed by aircraft during the North American electrical blackout of 2003. An air quality model produced substantial reductions in O3, but not as much as observed. The study highlights weaknesses in the model as commonly used for evaluating a single day event and suggests areas for further investigation. A new analysis and visualization method quantifies local-daily to hemispheric-seasonal scale relationships between weather and air pollution, confirming improved air quality despite increasing temperatures across the eastern U.S. Climate penalty factors indicate amplified smog formation in areas of the world with rising temperatures and increasing emissions. Tools developed in this dissertation provide data for model evaluation and methods for establishing air quality standards with an adequate margin of safety for cleaning the air and protecting the public's health in a world with changing climate.

Impact of Aircraft Emissions on Air Quality in the Vicinity of Airports. Volume I: Recent Airport Measurement Programs, Data Analyses, and Sub-Model Development

DTIC Science & Technology

1980-07-01

temperature has turbine stacks located in a region of high ambient turbu- - lence [Hoult (1975) and Egan (1975)]. I) I+ 110 4.5.4 Event Modeling Several...the aircraft industry in developing the gas turbine engine technology of the present era. Dispersion measurements permit determination of a power law...The ESEERCO Model for the Prediction of Plume Rise and Dispersion from Gas Turbine Engines. Air Pollution Control Association 68th Annual Meeting

Analysis of effect of flameholder characteristics on lean, premixed, partially vaporized fuel-air mixtures quality and nitrogen oxides emissions

NASA Technical Reports Server (NTRS)

Cooper, L. P.

1981-01-01

An analysis was conducted of the effect of flameholding devices on the precombustion fuel-air characteristics and on oxides of nitrogen (NOx) emissions for combustion of premixed partially vaporized mixtures. The analysis includes the interrelationships of flameholder droplet collection efficiency, reatomization efficiency and blockage, and the initial droplet size distribution and accounts for the contribution of droplet combustion in partially vaporized mixtures to NOx emissions. Application of the analytical procedures is illustrated and parametric predictions of NOx emissions are presented.

Contribution of air conditioning adoption to future energy use under global warming.

PubMed

Davis, Lucas W; Gertler, Paul J

2015-05-12

As household incomes rise around the world and global temperatures go up, the use of air conditioning is poised to increase dramatically. Air conditioning growth is expected to be particularly strong in middle-income countries, but direct empirical evidence is scarce. In this paper we use high-quality microdata from Mexico to describe the relationship between temperature, income, and air conditioning. We describe both how electricity consumption increases with temperature given current levels of air conditioning, and how climate and income drive air conditioning adoption decisions. We then combine these estimates with predicted end-of-century temperature changes to forecast future energy consumption. Under conservative assumptions about household income, our model predicts near-universal saturation of air conditioning in all warm areas within just a few decades. Temperature increases contribute to this surge in adoption, but income growth by itself explains most of the increase. What this will mean for electricity consumption and carbon dioxide emissions depends on the pace of technological change. Continued advances in energy efficiency or the development of new cooling technologies could reduce the energy consumption impacts. Similarly, growth in low-carbon electricity generation could mitigate the increases in carbon dioxide emissions. However, the paper illustrates the enormous potential impacts in this sector, highlighting the importance of future research on adaptation and underscoring the urgent need for global action on climate change.

Contribution of air conditioning adoption to future energy use under global warming

PubMed Central

Davis, Lucas W.; Gertler, Paul J.

2015-01-01

As household incomes rise around the world and global temperatures go up, the use of air conditioning is poised to increase dramatically. Air conditioning growth is expected to be particularly strong in middle-income countries, but direct empirical evidence is scarce. In this paper we use high-quality microdata from Mexico to describe the relationship between temperature, income, and air conditioning. We describe both how electricity consumption increases with temperature given current levels of air conditioning, and how climate and income drive air conditioning adoption decisions. We then combine these estimates with predicted end-of-century temperature changes to forecast future energy consumption. Under conservative assumptions about household income, our model predicts near-universal saturation of air conditioning in all warm areas within just a few decades. Temperature increases contribute to this surge in adoption, but income growth by itself explains most of the increase. What this will mean for electricity consumption and carbon dioxide emissions depends on the pace of technological change. Continued advances in energy efficiency or the development of new cooling technologies could reduce the energy consumption impacts. Similarly, growth in low-carbon electricity generation could mitigate the increases in carbon dioxide emissions. However, the paper illustrates the enormous potential impacts in this sector, highlighting the importance of future research on adaptation and underscoring the urgent need for global action on climate change. PMID:25918391

40 CFR 81.16 - Metropolitan Denver Intrastate Air Quality Control Region.

Code of Federal Regulations, 2014 CFR

2014-07-01

... (CONTINUED) AIR PROGRAMS (CONTINUED) DESIGNATION OF AREAS FOR AIR QUALITY PLANNING PURPOSES Designation of Air Quality Control Regions § 81.16 Metropolitan Denver Intrastate Air Quality Control Region. The Metropolitan Denver Intrastate Air Quality Control Region (Colorado) consists of the territorial area...

40 CFR 81.62 - Northeast Mississippi Intrastate Air Quality Control Region.

Code of Federal Regulations, 2012 CFR

2012-07-01

... (CONTINUED) AIR PROGRAMS (CONTINUED) DESIGNATION OF AREAS FOR AIR QUALITY PLANNING PURPOSES Designation of Air Quality Control Regions § 81.62 Northeast Mississippi Intrastate Air Quality Control Region. The Alabama-Mississippi-Tennessee Interstate Air Quality Control Region has been renamed the Northeast...

40 CFR 81.16 - Metropolitan Denver Intrastate Air Quality Control Region.

Code of Federal Regulations, 2013 CFR

2013-07-01

... (CONTINUED) AIR PROGRAMS (CONTINUED) DESIGNATION OF AREAS FOR AIR QUALITY PLANNING PURPOSES Designation of Air Quality Control Regions § 81.16 Metropolitan Denver Intrastate Air Quality Control Region. The Metropolitan Denver Intrastate Air Quality Control Region (Colorado) consists of the territorial area...

40 CFR 81.31 - Metropolitan Providence Interstate Air Quality Control Region.

Code of Federal Regulations, 2014 CFR

2014-07-01

... (CONTINUED) AIR PROGRAMS (CONTINUED) DESIGNATION OF AREAS FOR AIR QUALITY PLANNING PURPOSES Designation of Air Quality Control Regions § 81.31 Metropolitan Providence Interstate Air Quality Control Region. The Metropolitan Providence Interstate Air Quality Control Region (Rhode Island-Massachusetts) consists of the...

40 CFR 81.16 - Metropolitan Denver Intrastate Air Quality Control Region.

Code of Federal Regulations, 2012 CFR

2012-07-01

... (CONTINUED) AIR PROGRAMS (CONTINUED) DESIGNATION OF AREAS FOR AIR QUALITY PLANNING PURPOSES Designation of Air Quality Control Regions § 81.16 Metropolitan Denver Intrastate Air Quality Control Region. The Metropolitan Denver Intrastate Air Quality Control Region (Colorado) consists of the territorial area...